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Solve Business Problems Using Data Science Practically. Learn To Build &amp; Deploy Artificial Intelligence, Machine Learning, Data Science , Auto Ml, Deep Learning, Natural Language Processing (NLP) Web Applications Projects With Python (Flask, Django, Heruko, Streamlit Cloud).How much does a Data Scientist make in the United States?The national average salary for a Data Scientist is US$1,20,718 per year in the United States, 2.8k salaries reported, updated on July 15, 2021 (source: glassdoor)Salaries by Company, Role, Average Base Salary in (USD)Facebook Data Scientist makes USD 1,36,000/yr. Analyzed from 1,014 salaries.Amazon Data Scientist makes USD 1,25,704/yr. Analyzed from 307 salaries.Apple Data Scientist makes USD 1,53,885/yr. Analyzed from 147 salaries.Google Data Scientist makes USD 1,48,316/yr. Analyzed from 252 salaries.Quora, Inc. Data Scientist makes USD 1,22,875/yr. Analyzed from 509 salaries.Oracle Data Scientist makes USD 1,48,396/yr. Analyzed from 458 salaries.IBM Data Scientist makes USD 1,32,662/yr. Analyzed from 388 salaries.Microsoft Data Scientist makes USD 1,33,810/yr. Analyzed from 205 salaries.Walmart Data Scientist makes USD 1,08,937/yr. Analyzed 187 salaries.Cisco Systems Data Scientist makes USD 1,57,228/yr. Analyzed from 184 salaries.Uber Data Scientist makes USD 1,43,661/yr. Analyzed from 151 salaries.Intel Corporation Data Scientist makes USD 1,25,930/yr. Analyzed from 131 salaries.Airbnb Data Scientist makes USD 1,80,569/yr. Analyzed from 122 salaries.Adobe Data Scientist makes USD 1,39,074/yr. Analyzed from 109 salaries.<

Data is at the heart of our digital economy and data science has been ranked as the hottest profession of the 21st century. Whether you are new to the job market or already in the workforce and looking to upskill yourself, this five course Data Science with Python Professional Certificate program is aimed at preparing you for a career in data science and machine learning. No prior computer programming experience required!You will start by learning Python, the most popular language for data science. You will then develop skills for data analysis and data visualization and also get a practical introduction in machine learning. Finally, you will apply and demonstrate your knowledge of data science and machine learning with a capstone project involving a real life business problem.This program is taught by experts and focused on hands-on learning and job readiness. As such you will work with real datasets and will be given no-charge access to tools like Jupyter notebooks in the IBM Cloud. You will utilize popular Python toolkits and libraries such as pandas, numpy, matplotlib, seaborn, folium, scipy, Scikit-Learn, and more.Start developing data and analytical skills today and launch your career in data science!This course is highly practical but it won't neglect the theory. we'll start with python basics, and then understand the complete concept of environment , variables , loops , conditions and more advance concept of python programming and machine learning and we install the needed software (on Windows, Linux and Mac OS X), then we'll dive and start python programming straight away. From here onward you'll learn everything by example, by analyzing and practicing different concepts such as operator, operand, conditional statements, looping ,data management .etc, so we'll never have any boring dry theoretical lectures.The course is divided into a number of sections, each section covers a complete python programming field and complete machine lear

Join our interactive course, "Complete Guide to ChatGPT &amp; Copilot for Python &amp; R Projects", designed to give you hands-on experience in solving real data science problems using AI, Copilot, and ChatGPT. With Udemy's 30-day money-back guarantee in place, there's no need to worry if the class doesn't meet your expectations.The course is taught both in Python and R with RStudio. A complete installation guide on how to install and configure Python and R, was added in April 2024. It also explains how to connect RStudio to Python and use it as Python's IDE.Each lesson in this course stands alone, focusing on a different data science challenge.You'll learn how to apply AI tools like Copilot and ChatGPT to navigate through these challenges efficiently.Incorporating AI tools like Copilot and ChatGPT into your data science workflow can significantly enhance your speed and efficiency, often doubling (X2) or even increasing productivity tenfold (X10), depending on the task at hand.Here's what we'll cover, using practical, project-based learning:Data Clean-up and Tidy: Learn how to organize your data neatly, making it ready for analysis. This is a key step in data science to ensure your data is accurate and easy to work with. Using Pandas with Python and dplyr with R.Load Files of Different Formats: Discover how to bring in data from different kinds of files. This skill is important in data science because it lets you work with all sorts of data in tools like Copilot and ChatGPT.Data Visualization with Graphs: Find out how to use graphs to show your data in a clear and interesting way. Graphs help you see patterns and important points, which is a big part of data science.</

Interested in the field of Machine Learning, Deep Learning, and Artificial Intelligence? Then this course is for you!This course has been designed by a software engineer. I hope with the experience and knowledge I did gain throughout the years, I can share my knowledge and help you learn complex theories, algorithms, and coding libraries in a simple way.I will walk you step-by-step into Deep Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.This course is fun and exciting, but at the same time, we dive deep into Recurrent Neural Network. Throughout the brand new version of the course, we cover tons of tools and technologies including:Deep Learning.Google Colab Keras.Matplotlib.Splitting Data into Training Set and Test Set. Training Neural Network.Model building.Analyzing Results.Model compilation.Make a Prediction.Testing Accuracy.Confusion Matrix.ROC Curve.Text analysis.Image analysis.Embedding layers.Word embedding.Long short-term memory (LSTMs) models.Sequence-to-vector models.Vector-to-sequence models.Bi-directional LSTMs.Sequence-to-sequence models.Transforming words into feature vectors.frequency-inverse document frequency.Cleaning text data.Processing documents into tokens.Topic modelling with latent Dirichlet allocation Decomposing text documents with LDA.Autoencoder.Numpy.Pandas.TensorFlow.Sentiment Analysis.Matplotlib.out-of-core learnin

Common scenario: You try to get into machine learning and data science, but there's SO&nbsp;MUCH&nbsp;MATH.Either you never studied this math, or you studied it so long ago you've forgotten it all.What do you do?Well my friends, that is why I created this course.Linear Algebra is one of the most important math prerequisites for machine learning. It's required to understand probability and statistics, which form the foundation of data science.The "data" in data science is represented using matrices and vectors, which are the central objects of study in this course.If you want to do machine learning beyond just copying library code from blogs and tutorials, you must know linear algebra.In a normal STEM college program, linear algebra is split into multiple semester-long courses.Luckily, I've refined these teachings into just the essentials, so that you can learn everything you need to know on the scale of hours instead of semesters.This course will cover systems of linear equations, matrix operations (dot product, inverse, transpose, determinant, trace), low-rank approximations, positive-definiteness and negative-definiteness, and eigenvalues and eigenvectors. It will even include machine learning-focused material you wouldn't normally see in a regular college course, such as how these concepts apply to GPT-4, and fine-tuning modern neural networks like diffusion models (for generative AI&nbsp;art) and LL Ms (Large Language Models) using LoRA. We will even demonstrate many of the concepts in this course using the Python programming language (don't worry, you don't need to know Python for this course). In other words, instead of the dry old college version of linear algebra, this course takes just the most practical and impactful topics, and provides you with skills directly applicable to machine learning and data science, so you can s

Neste curso, você se aprofundará no universo da IA Generativa com LL Ms (Large Language Models), explorando o potencial da combinação entre LangChain e Python. Você implementará soluções proprietárias (ChatGPT) e modelos open source modernos, como Llama e Phi. Por meio de projetos práticos e reais, você desenvolverá aplicações inovadoras, incluindo um assistente virtual personalizado e um chatbot que interage com documentos e vídeos. Vamos explorar técnicas avançadas como RAG e agentes, além de utilizar ferramentas como Streamlit para criar interfaces intuitivas. Você aprenderá a utilizar essas tecnologias gratuitamente no Google Colab e também a executar os projetos em ambiente local.Na introdução, você será apresentado à teoria dos Grandes Modelos de Linguagem (LL Ms) e seus conceitos fundamentais. Além disso, será explorado o ecossistema da Hugging Face, que oferece soluções modernas de Processamento de Linguagem Natural (PLN). Você aprenderá a implementar LL Ms utilizando tanto o pipeline da Hugging Face quanto a biblioteca LangChain, compreendendo as vantagens de cada abordagem.Na segunda parte, será abordado o domínio da LangChain. Você aprenderá a acessar modelos de código aberto, como o Llama da Meta e o Phi da Microsoft, além de LL Ms proprietárias, como o ChatGPT da OpenAI. Será explicado como realizar a quantização de modelos, com o objetivo de melhorar a performance e a escalabilidade. Também serão apresentados os principais componentes do LangChain, como chains, templates e tools, e como utilizá-los para desenvolver soluções robustas em PLN. Técnicas de engenharia de prompt serão abordadas para ajudar a obter resultados mais precisos. O conceito de RAG (Retrieval-Augmented Generation) será explorado, incluindo o processo de armazenamento e recuperação de informações. Você aprenderá a implementar bancos de dados vetoriais (vector stores) e entenderá a importância dos embeddings e como utilizá-los de forma eficaz. Também será mostrado como usar RAG para inter

Atenção! Nas aulas deste curso é utilizada a versão 1.x do TensorFlow, sendo possível acompanhar as aulas utilizando essa versão. Adicionalmente, disponibilizamos o código atualizado considerando a versão 2.x. Em breve pretendemos regravar todas as aulas deste cursoA área de Deep Learning (Aprendizagem Profunda) está relacionada a aplicação das redes neurais artificiais na resolução de problemas complexos e que requerem artifícios&nbsp;computacionais avançados. Existem diversas aplicações práticas que já foram construídas utilizando essas&nbsp;técnicas, tais como:&nbsp;carros autônomos, descoberta de novos medicamentos,&nbsp;cura e diagnóstico antecipado de&nbsp;doenças, geração automática de notícias, reconhecimento facial, recomendação de produtos, previsão dos valores de ações na bolsa de valores e até mesmo a geração automática de roteiros de filmes! Nesses exemplos, a técnica base utilizada são as redes neurais artificiais, que procuram "imitar" como o cérebro humano funciona e são consideradas hoje em dia como as mais avançadas no cenário de Machine Learning (Aprendizagem de Máquina). E a maioria dessas aplicações foram desenvolvidas utilizando a biblioteca TensorFlow do Google, que hoje em dia é a ferramenta mais popular e utilizada nesse cenário. Por isso, é de suma importância que profissionais ligados à área de Inteligência Artificial e Machine Learning saibam como trabalhar com essa biblioteca, já que várias grandes empresas a utilizam em seus sistemas, tais como: Airbnd, Airbus, e Bay, Dropbox, Intel, IBM, Uber, Twitter, Snapchat e também o próprio Google!A área de Deep Learning&nbsp;é atualmente um dos campos de trabalho&nbsp;mais relevantes&nbsp;da Inteligência Artificial, sendo que o&nbsp;mercado de trabalho dessa área nos Estados Unidos e em vários países da Europa está em grande ascensão; e a previsão é que no Brasil cada vez mais esse tipo de profissional seja requisitado! Inclusive alguns estudos apontam que o

Welcome to my " Complete Python for Data Science &amp; Machine Learning from A-Z " course.Python with Machine Learning &amp; Data Science, Data Visulation, Numpy &amp; Pandas for Data Analysis, Kaggle projects from A-Z Python is a computer programming language often used to build websites and software, automate tasks, and conduct data analysis. Python is a general-purpose language, meaning it can be used to create a variety of different programs and isn't specialized for any specific problems.Python instructors at OAK Academy specialize in everything from software development to data analysis and are known for their effective, friendly instruction for students of all levels.Whether you work in machine learning or finance or are pursuing a career in web development or data science, Python is one of the most important skills you can learn.Python's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization. The core programming language is quite small and the standard library is also large. In fact, Python's large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasks.Do you want to learn one of the employer’s most requested skills? If you think so, you are at the right place. Python, machine learning, Django, python programming, machine learning python, python Bootcamp, coding, data science, data analysis, programming languages.We've designed for you "Complete Python for Data Science &amp; Machine Learning from A-Z” a straightforward course for the Complete Python programming langu

Selamat datang di program Pelatihan Data Science dengan Deep Learning dan Python Pelatihan ini diperuntukan untuk rekan - rekan ingin belajar data science dan machine learning dengan titik fokus pemanfaatan Deep Learning untuk model machine learning dan data science.Peserta diharapkan sudah menguasai pemrograman Python dasar implementasi machine learning dan data science dengan menggunakan Python. Kami juga menyediakan konten mengenai Pelatihan Data Science dan Machine Learning Dengan Python yang ada di Udemy ini.Seluruh konten didalam pelatihan ini dilaksanan secara step - by - step (langkah demi langkah) dan berurutan sehingga ini diharapkan semua peserta dapat dengan mudah mengikuti semua praktikum yang diberikan didalam pelatihan ini. Diharapkan semua peserta dapat mengikuti konten pelatihan ini secara berurutan ;).Berikut ini konten yang akan diberikan pada pelatihan ini.Persiapan pelatihan Konsep dan teori mengenai Deep Learning Pengenalan TensorFlow dan Keras Dasar Tensor TensorFlow Pemanfaatan GPU dan TPU pada komputasi TensorFlow dan Keras Pembuat Model dan Layer Untuk TensorFlow Training dan evaluasi Deep Learning pada TensorFlow Pengenalan dan instalasi PyTorch Pemanfaatan GPU dan TPU pada komputasi PyTorch Membangun model Deep Learning dengan PyTorch Training dan evaluasi Deep Learning pada PyTorch Penggunaan Tensor Board untuk visualisasi model pada TensorFlow dan PyTorch Penerapan Hyperparameter Tuning pada TensorFlow dan Keras Penerapan Hyperparameter Tuning pada PyTorch Penggunaan Tensor Board untuk implementasi Hyperparameter Kumpulan Studi Kasus Jika ada hal - hal yang ingin ditanyakan mengenai topik diatas, rekan - rekan dapat langsung ditulisnya di ruang diskusi pada we

As aplicações de Inteligência Artificial (IA) com Python têm desempenhado um papel significativo no setor financeiro, trazendo uma série de benefícios e transformando a forma como as instituições lidam com dados e tomam decisões. Aqui está um resumo da importância dessas aplicações em finanças:1. Tomada de Decisão Baseada em Dados:&nbsp; &nbsp;- A IA com Python capacita as instituições financeiras a tomar decisões mais informadas e precisas, utilizando algoritmos avançados para analisar grandes conjuntos de dados. Isso resulta em estratégias mais eficazes de investimento, gestão de riscos aprimorada e decisões mais fundamentadas.2. Previsão de Mercado e Tendências:&nbsp; &nbsp;- Algoritmos de machine learning e modelos de IA são utilizados para prever movimentos de mercado, identificar tendências e realizar análises preditivas. Isso auxilia investidores, traders e gestores de ativos na identificação de oportunidades e na mitigação de riscos.3. Detecção de Fraudes e Segurança:&nbsp; &nbsp;- Sistemas de IA são empregados para detectar padrões suspeitos e atividades fraudulentas em transações financeiras. Essa capacidade de análise em tempo real contribui para a segurança das transações e a proteção contra atividades fraudulentas.4. Gestão de Portfólio Automatizada:&nbsp; &nbsp;- Algoritmos de IA e aprendizado de máquina são usados para criar e otimizar automaticamente portfólios de investimento. Esses sistemas automatizados podem ajustar dinamicamente as alocações de ativos com base em condições de mercado em constante mudança.5. Atendimento ao Cliente e Chatbots:&nbsp; &nbsp;- A IA é aplicada em chatbots e assistentes virtuais para melhorar o atendimento ao cliente. Essas soluções são capazes de responder a consultas, fornecer informações sobre contas e até mesmo realizar transações simples, melhorando a eficiência e a experiência do cliente.6. Análise de Sentimento e Mí

Welcome to the Complete Deep Learning Course 2021 With 7+ Real Projects This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand. Other courses and tutorials have tended to stay away from pure TensorFlow and instead use abstractions that give the user less control. Here we present a course that finally serves as a complete guide to using the TensorFlow framework as intended, while showing you the latest techniques available in deep learning!This course is designed to balance theory and practical implementation, with complete google colab and Jupiter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way!This course covers a variety of topics, including Deep Learning.Google Colab Anaconda Jupiter Notebook Activation Function.Keras.Pandas.Seaborn.Feature scaling.Matplotlib.Scikit-Learn Sigmoid Function.Tanh Function.ReLU Function.Leaky Relu Function.Exponential Linear Unit Function.Swish function.Corpora.NLTK.TensorFlow 2.0Tokenization.Spacy.PoS tagging.NER.Stemming and lemmatization.Semantics and topic modelling.Sentiment analysis techniques.Lexicon-based methods.Rule-based methods.Statistical methods.Machine learning methods.</

Data Science and Machine Learning are the hottest skills in demand but challenging to learn. Did you wish that there was one course for Data Science and Machine Learning that covers everything from Math for Machine Learning, Advance Statistics for Data Science, Data Processing, Machine Learning A-Z, Deep learning and more? Well, you have come to the right place. This Data Science and Machine Learning course has 11 projects, 250+ lectures, more than 25+ hours of content, one Kaggle competition project with top 1 percentile score, code templates and various quizzes.We are going to execute following real-life projects,Kaggle Bike Demand Prediction from Kaggle competition Automation of the Loan Approval process The famous IRIS Classification Adult Income Predictions from US Census Dataset Bank Telemarketing Predictions Breast Cancer Predictions Predict Diabetes using Prima Indians Diabetes Dataset Today Data Science and Machine Learning is used in almost all the industries, including automobile, banking, healthcare, media, telecom and others. As the Data Science and Machine Learning practioner, you will have to research and look beyond normal problems, you may need to do extensive data processing. experiment with the data using advance tools and build amazing solutions for business. However, where and how are you going to learn these skills required for Data Science and Machine Learning? Data Science and Machine Learning require in-depth knowledge of various topics. Data Science is not just about knowing certain packages/libraries and learning how to apply them. Data Science and Machine Learning require an indepth understanding of the following skills,Understanding of the overall landscape of Data Science and Machine Learning Different types of Data Analy

Greetings,&nbsp;I am so excited&nbsp;to learn that you have started your path to becoming a&nbsp;Data Scientist &nbsp;with my course. Data Scientist is in-demand and&nbsp;most satisfying&nbsp;career, where you will solve the most interesting problems and challenges in the world. Not only, you will earn average salary of over $100,000 p.a., you will also see the&nbsp;impact of your work around your,&nbsp;is not is amazing?This is&nbsp;one of the most&nbsp;comprehensive course on any e-learning platform (including Udemy marketplace) which uses the power of Python to learn exploratory data analysis and machine learning algorithms. You will learn the skills to dive deep into the data and present solid conclusions for decision making.&nbsp;Data Science Bootcamps are costly, in thousands of dollars. However,&nbsp;this course&nbsp;is only a fraction of the&nbsp;cost of any such Bootcamp&nbsp;and includes&nbsp;HD lectures&nbsp;along with &nbsp;detailed&nbsp;code notebooks&nbsp;for every lecture. The course also includes&nbsp;practice&nbsp;exercises on real data&nbsp;for each topic you cover, because the goal is "Learn by Doing"!&nbsp;For your satisfaction, I would like to mention&nbsp;few topics that we will be learning in this course:Basis Python programming for Data Science Data Types, Comparisons Operators, if, else, elif statement, Loops, List Comprehension, Functions, Lambda Expression, Map and Filter Num PyArrays, built-in methods, array methods and attributes, Indexing, slicing, broadcasting &amp; boolean masking, Arithmetic Operations &amp; Universal Functions Pandas Pandas Data Structures - Series, DataF

Hi.. Hello and welcome to my new course, Machine Learning with Python for Dummies. We will discuss about the overview of the course and the contents included in this course.Artificial Intelligence, Machine Learning and Deep Learning Neural Networks are the most used terms now a days in the technology world. Its also the most mis-understood and confused terms too.Artificial Intelligence is a broad spectrum of science which tries to make machines intelligent like humans. Machine Learning and Neural Networks are two subsets that comes under this vast machine learning platform Lets check what's machine learning now. Just like we human babies, we were actually in our learning phase then. We learned how to crawl, stand, walk, then speak words, then make simple sentences.. We learned from our experiences. We had many trials and errors before we learned how to walk and talk. The best trials for walking and talking which gave positive results were kept in our memory and made use later. This process is highly compared to a Machine Learning Mechanism Then we grew young and started thinking logically about many things, had emotional feelings, etc. We kept on thinking and found solutions to problems in our daily life. That's what the Deep Learning Neural Network Scientists are trying to achieve. A thinking machine.But in this course we are focusing mainly in Machine Learning. Throughout this course, we are preparing our machine to make it ready for a prediction test. Its Just like how you prepare for your Mathematics Test in school or college. We learn and train ourselves by solving the most possible number of similar mathematical problems. Lets call these sample data of similar problems and their solutions as the 'Training Input' and 'Training Output' Respectively. And then the day comes when we have the actual test. We will be given new set of problems to solve, but very similar to the problems we learned, and based on th

Comprehensive Course Description:Electrification was undeniably one of the greatest engineering feats of the 20th century. The invention of the electric motor dates back to 1821, with mathematical analysis of electrical circuits following in 1827. However, it took several decades for the full electrification of factories, households, and railways to begin. Fast forward to today, and we are witnessing a similar trajectory with Artificial Intelligence (AI). Despite being formally founded in 1956, AI has only recently begun to revolutionize the way humanity lives and works.Similarly, Data Science is a vast and expanding field that encompasses data systems and processes aimed at organizing and deriving insights from data. One of the most important branches of AI, Machine Learning (ML), involves developing systems that can autonomously learn and improve from experience without human intervention. ML is at the forefront of AI, as it aims to endow machines with independent learning capabilities.Our "Data Science &amp; Machine Learning Full Course in 90 Hours" offers an exhaustive exploration of both data science and machine learning, providing in-depth coverage of essential concepts in these fields. In today's world, organizations generate staggering amounts of data, and the ability to store, analyze, and derive meaningful insights from this data is invaluable. Data science plays a critical role here, focusing on data modeling, warehousing, and deriving practical outcomes from raw data.For data scientists, AI and ML are indispensable, as they not only help tackle large data sets but also enhance decision-making processes. The ability to transition between roles and apply these methodologies across different stages of a data science project makes them invaluable to any organization.What Makes This Course Unique?This course is designed to provide both theoretical foundations and practical, hands-on experience. By the end of the

Programmer en Python pour la Data Science, le Machine Learning, la Data Viz et l'Intelligence Artificielle Ce cours a pour objectif de vous initier à la programmation en Python en lien avec les concepts essentiels du Big Data (Data Science, Machine Learning, IA, etc.). Il ne requiert aucun prérequis et vous permet d'atteindre un niveau solide en seulement 4 heures de formation.Acquérir des bases solides Plus besoin de partir à la chasse aux informations sur Google, l'essentiel de votre apprentissage est concentré dans ce cours.Gagner du temps Ce cours est conçu pour vous familiariser avec la Data Science et Python de manière rapide et efficace. Vous pourrez ainsi atteindre un niveau solide en seulement 4 heures de cours.Une formation qui va à votre rythme Les concepts sont présentés progressivement, à travers des exemples concrets issus de projets d'entreprises et d'universités, vous permettant d'appliquer ce que vous avez appris.Cours récent et régulièrement mis à jour Mis à jour récemment, ce cours est en adéquation avec les compétences actuellement recherchées par les entreprises.Éviter les pièges de débutants Ce cours détaille les bonnes pratiques d'un Data Scientist expérimenté pour rédiger un code de qualité professionnelle.Préparation réussie pour vos examens, certifications et tests techniques sur Python Les exercices inclus dans ce cours constituent un excellent moyen de préparation pour vos examens, certifications et tests techniques en entreprise.Travailler pour les plus grandes entreprises Des entreprises prestigieuses telles qu'Intel, Google, Netflix, Spotify, Meta, mais aussi Renault, la SNCF, Orange, Total, Capgemini, sont actuellement à la recherche de Data Scientists expérimentés maîtrisant Python.Se former à des métiers actuellement recherchés</stron

Jetzt neu: Zusätzlicher Bonus zum Thema Deep Learning (Neuronale Netze) mit Python, TensorFlow und Keras!Dieser Kurs enthält über 300 Lektionen, Quizze, Praxisbeispiele, ... - der einfachste Weg, wenn du Machine Learning lernen möchtest. Schritt für Schritt bringe ich dir maschinelles Lernen bei. In jedem Abschnitt lernst du ein neues Thema - zuerst die Idee / Intuition dahinter, und anschließend den Code sowohl in Python als auch in R.Machine Learning macht erst dann richtig Spaß, wenn man echte Daten auswertet. Deswegen analysierst du in diesem Kurs besonders viele Praxisbeispiele:Schätze den Wert von Gebrauchtwagen Schreibe einen Spam-Filter Diagnostiziere Brustkrebs Schreibe ein Programm, was die Bedeutung von Adjektiven lernt Lese Zahlen aus Bildern ein Alle Codebeispiele werden dir beiden Programmiersprachen gezeigt - du kannst also wählen, ob du den Kurs in Python, R, oder in beiden Sprachen sehen möchtest!Nach dem Kurs kannst du Machine Learning auch auf eigene Daten anwenden und eigenständig fundierte Entscheidungen treffen:Du weißt, wann welche Modelle in Frage kommen könnten und wie du diese vergleichst. Du kannst analysieren, welche Spalten benötigt werden, ob zusätzliche Daten benötigt werden, und weißt, die die Daten vorab aufbereitet werden müssen. Dieser Kurs behandelt alle wichtigen Themen:Regression Klassifizierung Clustering Natural Language Processing Bonus: Deep Learning (nur für Python, weil die Tools hier sehr viel ausgereifter sind)Zu allen diesen Themen lernst du verschiedene Algorithmen kennen. Die Ideen dahinter werden einfach erklärt - keine trockenen, mathematischen Formeln, sondern anschauliche, grafische Erklärungen.Wir verwenden hierbei g

Selamat datang di program pelatihan data science dan machine learning dengan Python!Pelatihan ini diperuntukan untuk rekan - rekan ingin belajar data science dan machine learning dari sudut terapan dengan memanfaatkan Python.Bagi rekan - rekan yang belum menguasai pemrograman Python, pelatihan juga memberikan konten pemrograman dasar untuk&nbsp; Python sehingga rekan - rekan dapat mengikuti pelatihan ini dengan baik. Bagi yang sudah bisa pemrograman Python, rekan - rekan dapat melanjutkan di topik berikutnya.Seluruh konten didalam pelatihan ini dilaksanan secara step - by - step (langkah demi langkah) dan berurutan sehingga ini diharapkan semua peserta dapat dengan mudah mengikuti semua praktikum yang diberikan didalam pelatihan ini. Diharapkan semua peserta dapat mengikuti konten pelatihan ini secara berurutan ;).Berikut ini konten yang akan diberikan pada pelatihan ini.Persiapan pelatihan Pemrograman Python Python Virtual Environment Pengolahan dan Analisa Data - Numpy dan Pandas Topik Khusus - Numpy dan Pandas - Database Visualisasi Data dengan memanfaatkan library Matplotlib, Seaborn dan Bokeh Topik Khusus Visualisasi Data Time Series Dataset, Pra-Proses dan Pengurangan Dimensi Feature (Dimensionality Reduction)Permasalahan dan Penyelesaian Kasus Linear Regression Permasalahan dan Penyelesaian Kasus Klasifikasi (Classification)Permasalahan dan Penyelesaian Kasus Kekelompokkan (Clustering)Hyperparameter Tuning Untuk Model Machine Learning Ensemble Methods Reinforcement Learning Automated Machine Learning (AutoML)Kumpulan Studi Kasus Jika ada hal - hal yang ingin ditanyakan mengenai topik diatas, rekan - rekan dapat langsung ditulisnya di ruang diskusi pada web ini sehingga rekan-rekan lainnya dapat mengetahui dan

3.997 / 5.000Aprender a programar en Python no siempre es fácil, especialmente si desea usarlo para la ciencia de datos. De hecho, hay muchas herramientas diferentes que deben aprenderse para poder usar correctamente Python para la ciencia de datos y el aprendizaje automático, y cada una de esas herramientas no siempre es fácil de aprender. Pero, este curso le dará todos los conceptos básicos que necesita sin importar para qué objetivo quiera usarlo, así que si: - Es estudiante y desea mejorar sus habilidades de programación y desea aprender nuevas utilidades sobre cómo usar Python - Necesidad de aprender los conceptos básicos de la ciencia de datos. - Debe comprender las herramientas básicas de ciencia de datos para mejorar su carrera. - Simplemente adquiera las habilidades para uso personal Entonces definitivamente te encantará este curso. No solo aprenderá todas las herramientas que se utilizan para la ciencia de datos, sino que también mejorará su conocimiento de Python y aprenderá a usar esas herramientas para poder visualizar sus proyectos. La estructura del curso Este curso está estructurado de manera que podrá aprender cada herramienta por separado y practicar programando en Python directamente con el uso de esas herramientas. De hecho, al principio aprenderá todas las matemáticas asociadas con la ciencia de datos. Esto significa que tendrá una introducción completa a la mayoría de las funciones y fórmulas estadísticas importantes que existen. También aprenderá a configurar y utilizar Jupyter, así como a escribir su código Python. Después, aprenderá las diferentes bibliotecas de Python que existen y cómo usarlas correctamente. Aquí aprenderás herramientas como Num Py o muchas otras.Finalmente, tendrá una introducción al aprendizaje automático y aprenderá cómo funciona un

Data Science is an interdisciplinary field that leverages statistical analysis, data exploration, and machine learning techniques to derive knowledge and meaningful insights from data.Definition of Data Science:Data Science encompasses various processes, including data acquisition, thorough analysis, and informed decision-making.Data Science involves the identification and interpretation of data patterns to make predictive assessments.Through the application of Data Science, organizations can achieve:1. Improved decision-making processes, enabling the selection between alternatives (A or B) with greater confidence.2. Predictive analysis that anticipates future events or trends, aiding in proactive planning.3. Discovery of hidden patterns and valuable information within datasets, leading to actionable insights.Applications of Data Science:Data Science finds extensive application across diverse industries such as banking, consultancy, healthcare, and manufacturing.Examples of Data Science applications include:1. Optimizing route planning for shipping purposes.2. Anticipating potential delays in flights, ships, trains, etc., through predictive analysis.3. Crafting personalized promotional offers for customers.4. Determining the best time to deliver goods for maximum efficiency.5. Forecasting future revenue for a company.6. Analyzing the health benefits of specific training regimens.7. Predicting election outcomes.Data Science Integration in Business:Data Science can be seamlessly integrated into various facets of business operations where relevant data is available, including:1. Consumer goods industries for market analysis and consumer behavior prediction.2. Stock markets for financial analysis and forecasting.3. Industrial settings for process optimization and quality control.4. Political scenarios for opinion

Готовы ли вы начать свой путь, чтобы стать Data Scientist?Специалист по анализу данных - одна из наиболее подходящих профессий для процветания в этом веке. Он цифровой, ориентированный на программирование и аналитический. Поэтому неудивительно, что спрос на специалистов по анализу данных на рынке труда растет.Однако предложение было очень ограниченным. Трудно получить навыки, необходимые для работы в качестве специалиста по данным.И как это сделать?Университеты не спешили создавать специализированные программы по науке о данных. (Не говоря уже о том, что существующие очень дороги и требуют много времени)Большинство онлайн-курсов сосредоточено на конкретной теме, и трудно понять, как навыки, которым они обучают, вписываются в общую картину.Этот всеобъемлющий курс станет вашим руководством к изучению того, как использовать возможности Python для анализа данных, создания красивых визуализаций и использования мощных алгоритмов машинного обучения! Курс регулярно пополняется новыми материалами!Этот курс подойдёт для всех - для начинающих без опыта программирования, для имеющих некоторый опыт программирования и для опытных разработчиков, стремящихся изучить Data Science!Вы научитесь программировать на Python, создавать удивительные визуализации данных и использовать машинное обучение с Python! Чему вы научитесь:Применять Python для Data ScienceИспользовать инструменты для работы в Data Science Научитесь использовать Num Py для числовых данныхНаучитесь использовать Pandas для анализа данныхНаучитесь использовать Matplotlib для визуализации данныхНаучитесь использовать Seaborn для визуализации данныхНаучитесь использовать встроенную визуализацию библиотеки PandasНаучитесь применять новые знания на практикеНаучитесь использовать библиотеки Machine LearningИ многое другое!Записывайтесь на курс и получите одну из самых востребованных профессий и супер

Would you like to learn how to detect if someone is wearing a Face Mask or not using Artificial Intelligence that can be deployed in bus stations, airports, or other public places?Would you like to build a Convolutional Neural Network model using Deep learning to detect Covid-19 Face Mask?If the answer to any of the above questions is "YES", then this course is for you.Enroll Now in this course and learn how to detect Face Mask on the static images as well as in the video streams using TensorFlow and OpenCV. As we know, COVID-19 has affected the whole world very badly. It has a huge impact on our everyday life, and this crisis is increasing day by day. In the near future, it seems difficult to eradicate this virus completely.To counter this virus, Face Masks have become an integral part of our lives. These Masks are capable of stopping the spread of this deadly virus, which will help to control the spread. As we have started moving forward in this ‘new normal’ world, the necessity of the face mask has increased. So here, we are going to build a model that will be able to classify whether the person is wearing a mask or not. This model can be used in crowded areas like Malls, Bus stands, and other public places.This is a hands-on Data Science guided project on Covid-19 Face Mask Detection using Deep Learning and Computer Vision concepts. We will build a Convolutional Neural Network classifier to classify people based on whether they are wearing masks or not and we will make use of OpenCV to detect human faces on the video streams. No unnecessary lectures. As our students like to say :"Short, sweet, to the point course"The same techniques can be used in :Skin cancer detection Normal pneumonia detection Brain

Hello there,Welcome to the “Machine Learning Python with Theoretically for Data Science” course.Machine Learning with Python in detail both practically and theoretically with machine learning project for data science Machine learning courses teach you the technology and concepts behind predictive text, virtual assistants, and artificial intelligence. You can develop the foundational skills you need to advance to building neural networks and creating more complex functions through the Python and R programming languages. Machine learning training helps you stay ahead of new trends, technologies, and applications in this field.Machine learning describes systems that make predictions using a model trained on real-world data. For example, let's say we want to build a system that can identify if a cat is in a picture. We first assemble many pictures to train our machine learning model. During this training phase, we feed pictures into the model, along with information around whether they contain a cat. While training, the model learns patterns in the images that are the most closely associated with cats. This model can then use the patterns learned during training to predict whether the new images that it's fed contain a cat. In this particular example, we might use a neural network to learn these patterns, but machine learning can be much simpler than that. Even fitting a line to a set of observed data points, and using that line to make new predictions, counts as a machine learning model.Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries and new problems. machine learning, python, data science, machine learning python, python data science, machine learning a-z, python for data science and machine learning bootcamp, python for data science, complete machine learning, machine learning projects,Use Scikit-Learn, Num Py, Pandas, Matpl

Machine learning and Deep learning have revolutionized various industries by enabling the development of intelligent systems capable of making informed decisions and predictions. These technologies have been applied to a wide range of real-world projects, transforming the way businesses operate and improving outcomes across different domains.In this training, an attempt has been made to teach the audience, after the basic familiarity with machine learning and deep learning, their application in some real problems and projects (which are mostly popular and widely used projects).Also, all the coding and implementation of the models are done in Python, which in addition to machine learning, students' skills in Python language will also increase and they will become more proficient in it.In this course, students will be introduced to some machine learning and deep learning algorithms such as Logistic regression, multinomial Naive Bayes, Gaussian Naive Bayes, SGD Classifier, ... and different models. Also, they will use artificial neural networks for modeling to do the projects.The use of effective data sets in different fields, data preparation and pre-processing, visualization of results, use of validation metrics, different prediction methods, image processing, data analysis and statistical analysis are other parts of this course.Machine learning and deep learning have brought about a transformative impact across a multitude of industries, ushering in the creation of intelligent systems with the ability to make well-informed decisions and accurate predictions. These innovative technologies have been harnessed across a diverse array of real-world projects, reshaping the operational landscape of businesses and driving enhanced outcomes across various domains.Within this training course, the primary aim is to impart knowledge to the audience, assuming a foundational understanding of machine learning and deep learning concepts. The focus then

La Visión por Computador o Computer Vision (en inglés) es uno de los primeros objetivos que tuvo la programación desde sus inicios y, sobre todo, desde que se planteó la utilización del procesado automático en las cadenas de montaje. Desde discriminar la madurez de las frutas por su color, hasta reconocer patrones biométricos, pasando por los pulsómetros ópticos, o el reconocimiento de matrículas. Las utilidades de la Visión por Computador están sólo limitadas por la imaginación humana. En los últimos años, con el aumento del conocimiento en la denominada Ciencia de los Datos, se han desarrollados nuevos (y no tan nuevos) métodos de Aprendizaje para que sean las máquinas las que puedan tomar decisiones en base al procesado de la imagen que sus ojos tecnológicos les proporciona. El Machine Learning y, el siguiente paso, el Deep Learning ha supuesto una ventaja mayor si cabe en la autonomía de las máquinas. Trabajaremos con un famoso set de datos denominado MNIST, y que contiene 60.000 ejemplos de números manuscritos con su correspondiente etiqueta del número que representan. Cada número esta formado por una matriz de píxeles de 28x28 con valores entre 0 y 255 para la intensidad del trazo. En el curso vamos a analizar una buena cantidad de métodos y algoritmos de Machine Learning, como Naïve Bayes, Random Forest, Support Vector Machine, K Nearest Neighbours o Redes Neuronales y sistemas de pre-procesado de la información, como PCA, SVD o HOG. También trabajaremos algunos sistemas de Deep Learning, como H2O o TensorFlow (de Google) para el tratamiento de esta información de imágenes. Espero que os guste el curso y que disfrutéis aprendiendo los entresijos de la Visión por Computador y el Aprendizaje Profundo y Automático.

Science des Données et Apprentissage Automatique : Compréhension Théorique Approfondie La science des données (Data Science) est un domaine vaste et fascinant, tandis que l'apprentissage automatique (Machine Learning) est une branche passionnante de la Data Science. Ce cours de deux heures offre une exploration détaillée de ces domaines pour ceux qui souhaitent comprendre leur fonctionnement.Ce cours se distingue par son approche visuelle et simplifiée, qui démystifie les concepts et algorithmes de l'apprentissage automatique sans se perdre dans les détails mathématiques. Il se concentre sur la théorie, offrant une base solide pour quiconque souhaite exceller dans le domaine de la science des données.Les sections de ce cours sont interconnectées et progressives, formant un ensemble cohérent qui facilite l'apprentissage. Chaque section se construit sur les précédentes, vous permettant d'explorer des concepts de plus en plus avancés au fur et à mesure de votre progression.Ce cours aborde les compétences les plus recherchées dans le monde réel de la science des données et de l'apprentissage automatique. Il est conçu pour être simple, facile à comprendre, et descriptif, vous permettant de progresser rapidement.Rejoignez ce cours pour démystifier la science des données et l'apprentissage automatique. C'est une opportunité unique d'acquérir des connaissances solides dans un format accessible et inspirant !Contenu du cours :Après avoir suivi ce cours avec succès, vous serez en mesure de :Comprendre les concepts, principes et théories de la science des données et de l'apprentissage automatique Appréhender la méthodologie de la science des données et de l'apprentissage automatiqueÉvaluer les avantages et les inconvénients des différents algorithmes d'apprentissage automatiqueSélectionner l'algorithme d'apprentissage automat

Yapay zeka alanına giriş yapmak ve "öğrenen" uygulamalar geliştirmek istiyorsanız derin öğrenme yöntemlerini öğrenmek için sizi temelden ileri seviyeye kadar teorik anlatım ve pratik uygulamaları içeren bu kapsamlı "Derin Öğrenmeye Giriş" eğitimime davet ediyorum.Eğitimi bitirdiğinizde, derin öğrenmenin temellerini, yapay sinir ağı modelleri oluşturma ve geliştirme adımlarını ve başarılı yapay öğrenme projelerini nasıl gerçekleştirebileceğinizi öğreneceksiniz. Uygulayacağımız yöntemler:Temel yapay sinir ağları, Evrişimli sinir ağları (CNNs), Özyinelemeli sinir ağları (RNNs), Uzun-kısa vadeli bellek modeli (LSTMs), Makine öğrenmesinde optimizasyon ve regülarizasyon yöntemlerini, Kapsül ağları, Pekiştirmeli öğrenme (RL), Çekişmeli üretici ağları (GANs) Tüm bu yöntemleri Python programlama dili kullanarak TensorFlow ve gerisinde çalışan Keras kütüphanelerinde uygulayacaksınız. Yapay zeka ve derin öğrenme çoklu endüstrileri geliştirmekte ve dönüştürmektedir. Bu dersi tamamladıktan sonra, bunu işinize uygulamak için yaratıcı yollar bulabilirsiniz.

Para entender cómo organizaciones como Google, Amazon e incluso Udemy utilizan el Machine Learning y la inteligencia artificial (IA) para extraer el significado y los conocimientos de enormes conjuntos de datos , este curso de Machine Learning te proporciona lo esencial. Según Glassdoor y Indeed, los científicos de datos ganaron un sueldo medio de 120.000 dólares, ¡y eso es solo la norma!Cuando se trata de ser atractivo, los científicos de datos ya lo son. En un mercado laboral altamente competitivo, es difícil retenerlos una vez contratados. Las personas con una mezcla única de formación científica, experiencia informática y capacidad de análisis son difíciles de encontrar.Al igual que los "quants" de Wall Street de los años ochenta y noventa, se espera que los científicos de datos de hoy en día tengan un conjunto de habilidades similares. Las personas con formación en física y matemáticas acudieron a los bancos de inversión ya los fondos de cobertura en aquella época porque pudieron idear algoritmos y métodos de datos novedosos.Dicho esto, la ciencia de los datos se está convirtiendo en una de las ocupaciones más adecuadas para el éxito en el siglo XXI. Se trata de una profesión informatizada, basada en la programación y de naturaleza analítica. Por lo tanto, no es de extrañar que la necesidad de científicos de datos haya preocupado en el mercado laboral en los últimos años.La oferta, en cambio, ha sido bastante restringida. Es un reto conseguir los conocimientos y habilidades necesarios para ser contratado como científico de datos .En este curso, las notaciones y matemáticas la jerga se reducen a lo más básico, cada tema se explica en un lenguaje sencillo, lo que facilita su comprensión. Una vez que tengas en tus manos el código, podrás jugar con él y construir sobre él. El énfasis de este curso está en entender y usar estos algoritmos en el mu

Atenção: nesse curso ainda estão sendo adicionadas aulas! Machine Learning (aprendizado de máquina) é uma área que representa uma evolução nos campos de Ciência da Computação, Análise de Dados, Engenharia de Software e Inteligência Artificial. Nesse curso você aprenderá Machine Learning com a linguagem de Programação Python. Não é preciso ter conhecimento em Python, pois o curso possui uma seção para quem é iniciante na linguagem. Além disso, o curso trata das principais bibliotecas para análise de dados e utilização de técnicas de aprendizado de máquina tais como Num Py, Pandas, Scikit-Learn e Matplotlib. Também serão explicadas técnicas de aprendizado de máquina para facilitar o entendimento e utilização das mesmas nos exemplos práticos. Todo o curso é 100% em vídeo-aulas, tem direito a certificado e acesso vitalício! Os instrutores Marcos Castro (mais de 12 mil alunos na Udemy) e Gileno Filho (mais de 10 mil alunos na Udemy) irão estar disponíveis para tirar quaisquer dúvidas através do fórum do curso. O que está esperando? Machine Learning é utilizado por empresas ao redor do mundo para facilitar a análise de dados. Vivemos a era do Big Data, o volume de dados produzidos é gigantesco e precisamos de técnicas para automatizar e nos ajudar a encontrar algum padrão nesses dados de forma que possamos resolver os problemas. Aguardamos você no curso!

Este curso básico de TensorFlow te enseñará a crear redes neuronales para Deep Learning o aprendizaje profundo.Es una guía fácil con muchos ejemplos, para entener las complejidades del marco de TensorFlow de Google.Este curso está repleto de ejemplos escritos en Python sobre Jupyter Notebook, para que puedas probarlos tu mismo.Estos son los temas tratados en este curso de TensorFlow :- Introduccion al Machine Learning- Instalacion del entorno de trabajo- Curso básico de Python sobre las librerías usadas en este curso: - Num Py - Pandas - Matplotlib - Scikit-Learn- Introducción a las redes neuronales (Deep Learning) - Neuronas y perceptrones - Funciones de activacion - Funciones de coste - Algoritmo del gradiente descendiente - Practicar con una red neuronal en el navegador- TensorFlow - Introducción a TensorFlow - Sintaxis básica de TensorFlow - Grafos en TensorFlow - Grafos por defecto - Variables y placeholders - Ejemplo de red neuronal - parte 1 - Ejemplo de red neuronal - parte 2 - Ejemplo de regresión simple con TensorFlow - Ejemplo de clasificación con TensorFlow - Ejemplo de regresión con TensorFlow - parte 1 - Ejemplo de regresión con TensorFlow - parte 2 - Ejemplo de regresión con TensorFlow - parte 3- Redes Neuronales Convolucionales - Introducción a las redes neuronales convolucionales - MNIST - Base de datos de imágenes de dígitos escritos a mano - Ejemplo con MNIST - Importar base de datos y mostrar una imagen- Redes Neuronales Recurrentes - Introducción a las redes neuronales recurrentes - Ejemplo de una red neuronal recurrente con TensorFlow - Ejemplo de series temporales - parte 1 - Ejemplo de series temporales - parte 2 - Ejemplo de series temporales - parte 3- Bibiliotecas - Estimator API - Ke

Data Scientist wurde von Glassdoor als Nummer 1 Job gerankt und erzielt laut Indeed einen überdurchschnittlichen Gehalt. Die Karriere im Bereich Data Science ist eine bereichernde Tätigkeit und erlaubt es euch an den größten und interessantesten Herausforderungen der Welt zu arbeiten.&nbsp;Dieser Kurs richtet sich sowohl an Anfänger, die zum ersten Mal mit der Programmiersprache R in Berührung kommen, als auch für erfahrene Entwickler, die ihr Portfolio um Fähigkeiten in Richtung R, Data Sciene und Machine Learning ausbauen wollen!&nbsp; &nbsp;"Perfekter Einstieg in die Sprache R. Zuvor hatte ich keine Kenntnis dieser Sprache. Gut gefällt mir, dass direkt auch Data Science Anwendungen inbegriffen sind, da ich diese beruflich brauche. Top! (★★★★★ D. Mika)Dieser umfangreiche Kurs ist vergleichbar mit anderen Data Science Bootcamps die mehrere tausend Euro kosten. Das alles findest du in über 120 HD Video Lektionen und detaillierten Code Notebooks zu jeder Lektion. Dies macht diesen Kurs zum umfangreichsten Data Science Kurs mit R auf Udemy!Wir werden gemeinsam lernen, wie man mit R programmiert, grandiose Visualisierungen erstellt und mit echten Daten und echte Data Science Fälle umgeht. Dazu verwenden wir R-Studio und das Jupyter Notebook mit R. Hier ist eine Übersicht einiger Themen:Programmieren mit RFortgeschrittene Programmierung in RR Date Frames zur Lösung komplexer Aufgaben verwenden Mit R Excel Datein bearbeiten Web Scraping mit RR mit SQL verbindenGG Plot2 zur Visualisierung verwendenÜbersicht und Einsatz&nbsp;von DplyR&nbsp;und TidyRPlotly für interaktive Visualisierungen verwenden Analysiere echte Daten an&

Neste curso, exploramos o vasto mundo de Data Science e Machine Learning, focando na base lógica e matemática por trás dos principais algoritmos utilizados na área. Veremos como funcionam os principais algoritmos de Regressão, Classificação, Clusterização, NLP, Deep Learning, Regras de Associação, Algoritmos Genéticos, Séries&nbsp;Temporais e muito mais - sem exagerar no "matematiquês". O curso foi pensado de forma a ser o mais democrático possível, servindo como porta de entrada para pessoas que queiram aprender de verdade os principais conceitos antes de entrar no mercado, pessoas que já estejam trabalhando com ciência de dados mas se veem com dificuldades de entender como funcionam os modelos, ou pessoas que simplesmente se interessam pela área e gostariam de aprender como funciona - não necessariamente visando adentrar o mercado. Até por isso, o curso não é tão orientado a código; ao invés de criar código para cada modelo e cada técnica mostrada, ao final do curso há uma seção com alguns projetos da vida real, em que podemos ver tanto como o código é feito, mas, principalmente, como é o raciocínio e as decisões tomadas para resolver problemas de dados.Também trago uma seção bastante rica e dedicada a explicar como se "produtizam" modelos em empresas, falando sobre coisas como deploy, monitoramento, construção de features, pré-processamento, definição de um projeto de ML, expectativa e visão do mercado, progressão de carreira e muito mais!O curso ainda tem um "crash course" de Python, opcional para quem já programa na linguagem, mas valiosa para aqueles que precisam de uma base mais sólida.

Este curso pretende ser una introducción a las técnicas más relevantes de Machine Learning y mostrar ejemplos de aplicación de estas técnicas. Que sirva para conocer qué técnicas existen, en qué se fundamentan y sobre qué tipos de problemas pueden aplicarse. El enfoque será teórico-práctico y se hará uso del lenguaje de programación Python y del toolkit Scikit-Learn. Se recomienda a los alumnos instalarse ANACONDA en su plataforma habitual. ANACONDA incluye Python, Scikit-Learn y Matplotlib. La versión de python que utilizaremos será la 3.6.También veremos pyspark como plataforma de desarrollo de aplicaciones distribuídas Entre los principales objetivos podemos destacar:Introducir los conceptos de ciencias de datos y machine learning.Introducir las principales librerías que podemos encontrar en python para aplicar técnicas de machine learning a los datos.Introducir las principales librerías que podemos encontrar en python para tratamiento y visualización de datos Dar a conocer los pasos para construir un modelo de machine learning, desde la adquisición de datos,pasando por la generación de funciones, hasta la selección de modelos.Dar a conocer los principales algoritmos para resolver problemas de machine learning.Introducir Scikit-Learn como herramienta para resolver problemas de machine learning.Introducir pyspark como herramienta para aplicar técnicas de big data y map-reduce a los datos.Conocer y aplicar algoritmos de machine learning con pyspark.Introducir los sistemas de recomendación basados en contenidos

Welcome to the course wine quality prediction! In this course you will learn how to work with data from end-to-end and create a machine learning model that predicts the quality of wines.This data set contains records related to red and white variants of the Portuguese Vinho Verde wine. It contains information from 1599 red wine samples and 4898 white wine samples. Input variables in the data set consist of the type of wine (either red or white wine) and metrics from objective tests (e.g. acidity levels, PH values, ABV, etc.).It is super important to notice that you will need python knowledge to be able to understand this course. You are going to develop everything using Google Colab, so there is no need to download Python or Anaconda. You also need basic knowledge of Machine Learning and data science, but don't worry we will cover the theory and the practical needs to understand how each of the models that we are going to use work.In our case, we will work with a classification problem (a set from the supervised learning algorithms). That means that we will use the quality as the target variable and the other variables as the inputs. In this sense, we will some examples to train our model and predict the quality of other wines.You will learn to work with Decision Trees, Logistic Regression, how to use Lazy Predict and how to tune the hyperparameters using Grid Search.

A área de Machine Learning (Aprendizagem de Máquina) e Data Science (Ciência de Dados) é atualmente um dos campos de trabalho&nbsp;mais relevantes&nbsp;da Inteligência Artificial, sendo responsável pela utilização de algoritmos inteligentes que tem a função de fazer com que os computadores aprendam por meio de bases de dados. O mercado de trabalho de Machine Learning nos Estados Unidos e em vários países da Europa está em grande ascensão; e a previsão é que no Brasil cada vez mais esse tipo de profissional seja requisitado! Inclusive alguns estudos apontam que o conhecimento dessa área será em breve um pré-requisito para os profissionais de Tecnologia da Informação! E dentro deste contexto está o cientista de dados, que já foi classificado como o trabalho "número 1" por vários veículos da mídia internacional.E para levar você até essa área, neste curso completo você terá uma visão teórica e prática sobre os principais algoritmos de machine learning utilizando o Python, que é uma das linguagens de programação mais relevantes nesta área. Além disso, vamos utilizar o Google Colab para a implementação dos exemplos, o que facilita o entendimento dos conceitos e evita problemas de instalação de bibliotecas. Este curso é considerado de A à Z pelo fato de apresentar desde os conceitos mais básicos até técnicas mais avançadas, de modo que ao final você terá todas as ferramentas necessárias para construir soluções complexas e que podem ser aplicadas em problemas do dia-a-dia das empresas! Você aprenderá tudo passo a passo, ou seja, tanto a teoria quanto a prática de cada algoritmo! O curso é dividido em cinco partes principais:Classificação - pré-processamento dos dados, naïve bayes, árvores de decisão, random forest, regras, regressão logística, máquinas de vetores de suporte (SVM), redes neurais artificiais, avaliação de algoritmos e combinação e rejeição de classificadores Regressão - regressão linear simples e múltipla, polinomial, árvores de dec

You don’t want to code, but you do want to know about Big Data, Artificial Intelligence and Machine Learning? Then this course is for you!You do want to code and you do want to learn more about Machine Learning, but you don’t know how to start? Then this course is for you!The goal of this course is to get you as smoothly as possible into the World of Machine Learning. All the buzzwords will now be clear to you. No more confusion about “What’s the difference between Machine Learning and Artificial Intelligence.” No more stress about “This is just too much information. I don’t know where to start”The topics in this course will make it all clear to you. They are :Part 1 - Welcome Part 2 - Why machine learning?Part 3 - Buzzwords Part 4 - The Machine Learning Process Part 5 - Conclusion But it does not have to end here. As a bonus, this course includes references to the courses which I find the most interesting. As well as other resources to get you going.

Dans ce cours, vous allez découvrir et approfondir les différents aspects liés à l'apprentissage automatique avec Python. Nous utiliserons les librairies telles que TensorFlow, Keras, Pandas, Numpy, Scikit-Learn, ...Les travaux sont accessibles et exploitables en ligne grâce à l'utilisation des carnets Jupyter avec Google Colab. Aucune installation de logiciel spécifique sur son ordinateur n'est requise car tout le travail se fait en ligne.A chaque étape d'apprentissage de ce cours, de nouveaux modèles sont introduits. Des explications claires permettent de bien les comprendre à travers 6 thèmes d'étude :Structure de base d'un réseau de neurones Reconnaissance d'image avec un réseau de neurones à convolution 2DTraitement d'image avec un réseau de neurones profond à convolution 2DSystèmes de recommandations et d'analyse des ressentisDétection d'anomalies dans les données Analyse et prédiction sur les séries temporelles Les activités en Python expliquent clairement comment les exploiter. Des exercices sont régulièrement proposés pour consolider votre apprentissage.D'une durée totale de 19,5 heures, ce cours vous permettra d'être à l'aise avec les outils actuels du Deep Learning. Vous serez alors capable d'utiliser ces ressources pour créer vos propres projets et d'approfondir avec sérénité et en autonomie vos connaissances dans ce domaine.=== Prérequis ===Vous n'avez pas besoin d'être un spécialiste du langage Python. En effet, au fur et à mesure de votre progression, vous manierez ce langage et découvrirez les subtilités liées à son utilisation.Si vous êtes complètement débutant en Deep Learning, alors ce cours est fait pour vous. Ce cours est structuré de manière progressive pour acquérir petit à petit les bases du de

En este curso se enseñan todos los conocimientos necesarios para convertirse en un Data Scientist (Científico de Datos). Para ello usaremos el lenguaje de Programación Python como herramienta, ya que es uno de los lenguajes con más demanda hoy en dia.En concreto, se tratarán en profundidad los siguientes apartados:- Programación en Python, donde aprendemos a programar en uno de los lenguajes más populares hoy en día como es Python.- Análisis de Datos, donde aprenderemos como realizar un Análisis Exploratorio de Datos, usando técnicas estadísticas y de Visualización de Datos.- Machine Learning, donde aprenderemos como crear modelos predictivos, evaluarlos y usarlos en un entorno de desarrollo.- Deep Learning, donde nos enfocamos en la creación de Redes Neuronales.- Web Scraping, donde aprenderemos técnicas para extraer información de páginas web.- Big Data, donde aprenderemos a como procesar datasets de gran tamaño asi como entrenar modelos predictivos con ellos.

Der Kurs ist ein Einsteiger-Kurs in die Welt des Data Science, des Machine Learning, der künstlichen Intelligenz und dem Arbeiten mit Daten. In Zeiten der Digitalisierung und der digitalen Transformation stellt die Wissenschaft der Daten (Data Science) immer mehr eine zentrale Disziplin dar.&nbsp;Ohne grundlegende Kenntnisse und Qualifikationen im Bereich der Daten sind viele Arbeitsplätze kaum noch denkbar.Der Kurs liefert daher einen unkomplizierten Einstieg in die Welt der Daten und der Algorithmen. Dadurch ergibt sich ein Grundverständnis, was Daten überhaupt sind und man sie einer automatischen Verarbeitung mit Algorithmen zugänglich machen kann. Alle Algorithmen und mathematischen Verfahren werden Schritt für Schritt erklärt.Der Lernpfad dieses Kurses besteht u.a. aus folgenden Abschnitten:- Was sind Daten?- Datentypen, Data Mining und Visualisierung von Daten- Statistische Grundbegriffe- Einfache Clustering-Verfahren- Lineare und logistische Regression- Kurze Einführung in die Graphentheorie- Entscheidungsbäume und Random Forest- Einführung in die neuronalen Netze- Überblick über generative KI und deren Anwendungen Alle Algorithmen und Verfahren werden so ausführlich erläutert, dass keine speziellen mathematischen Vorkenntnisse oder IT-Fähigkeiten erforderlich sind. Ein grundlegendes Interesse an mathematischen Zusammenhängen wird hingegen vorausgesetzt. Die Beispiele stehen im Quellcode in der Programmiersprache Python zum Download und zum selber ausprobieren bereit.Der Kurs richtet sich insbesondere an Fach- und Führungskräfte, die selbst mit Daten arbeiten und sich ein tieferes Verständnis grundlegender Zusammenhänge erarbeiten möchten.

A área de Machine Learning (Aprendizagem de Máquina) é atualmente um dos campos de trabalho mais relevantes da Inteligência Artificial, sendo responsável pela utilização de algoritmos inteligentes que tem a função de fazer com que os computadores aprendam por meio de bases de dados. O mercado de trabalho de Machine Learning nos Estados Unidos e em vários países da Europa está em grande ascensão; e a previsão é que no Brasil cada vez mais esse tipo de profissional seja requisitado! Inclusive alguns estudos apontam que o conhecimento dessa área será em breve um pré-requisito para os profissionais de Tecnologia da Informação! E dentro deste contexto está o cientista de dados, que já foi classificado como o trabalho "número 1" por vários veículos da mídia internacional.E para levar você até essa área, neste curso completo você terá uma visão teórica e prática sobre os principais algoritmos de machine learning utilizando a ferramenta Weka, que é uma das ferramentas mais utilizadas para machine learning e mineração de dados. Além disso, também utilizaremos a linguagem de programação Java para fazer a integração com o Weka! Este curso apresenta desde os conceitos mais básicos até técnicas mais avançadas, de modo que ao final você terá todas as ferramentas necessárias para construir soluções complexas e que podem ser aplicadas em problemas do dia-a-dia das empresas! Você aprenderá tudo passo a passo, ou seja, tanto a teoria quanto a prática de cada algoritmos! O curso é dividido em cinco partes:Classificação - extração de características de imagens, naive bayes, árvores de decisão, random forest, regras, regressão logística, máquinas de vetores de suporte (SVM), redes neurais artificiais, avaliação de algoritmos e combinação e rejeição de classificadores Regressão - regressão linear simples e múltipla, polinomial, árvores de decisão, random forest, vetores de suporte e redes neurais artificiais Regras de associação - algoritmo aprio

A área de Machine Learning (Aprendizagem de Máquina) é atualmente um dos campos de trabalho mais relevantes da Inteligência Artificial, sendo responsável pela utilização de algoritmos inteligentes que tem a função de fazer com que os computadores aprendam por meio de bases de dados. O mercado de trabalho de Machine Learning nos Estados Unidos e em vários países da Europa está em grande ascensão; e a previsão é que no Brasil cada vez mais esse tipo de profissional seja requisitado! Inclusive alguns estudos apontam que o conhecimento dessa área será em breve um pré-requisito para os profissionais de Tecnologia da Informação! E dentro deste contexto está o cientista de dados, que já foi classificado como o trabalho "número 1" por vários veículos da mídia internacional.E para levar você até essa área, neste curso completo você terá uma visão teórica e prática sobre os principais algoritmos de machine learning utilizando o R, que é uma das linguagens de programação mais relevantes nesta área de ciência de dados. Este curso é considerado de A à Z pelo fato de apresentar desde os conceitos mais básicos até técnicas mais avançadas, de modo que ao final você terá todas as ferramentas necessárias para construir soluções complexas e que podem ser aplicadas em problemas do dia-a-dia das empresas! Você aprenderá tudo passo a passo, ou seja, tanto a teoria quanto a prática de cada algoritmos! O curso é dividido em cinco partes principais:Classificação - pré-processamento dos dados, naive bayes, árvores de decisão, random forest, regras, regressão logística, máquinas de vetores de suporte (SVM), redes neurais artificiais, avaliação de algoritmos e combinação e rejeição de classificadores Regressão - regressão linear simples e múltipla, polinomial, árvores de decisão, random forest, vetores de suporte (SVR) e redes neurais artificiais Regras de associação - algoritmos apriori e ECLAT Agrupamento - k-means, agrupamento hierárqu

Common scenario: You try to get into machine learning and data science, but there's SO MUCH MATH.Either you never studied this math, or you studied it so long ago you've forgotten it all.What do you do?Well my friends, that is why I created this course.Probability is one of the most important math prerequisites for data science and machine learning. It's required to understand essentially everything we do, from the latest LL Ms like ChatGPT, to diffusion models like Stable Diffusion and Midjourney, to statistics (what I like to call "probability part 2").Markov chains, an important concept in probability, form the basis of popular models like the Hidden Markov Model (with applications in speech recognition, DNA analysis, and stock trading) and the Markov Decision Process or MDP (the basis for Reinforcement Learning).Machine learning (statistical learning) itself has a probabilistic foundation. Specific models, like Linear Regression, K-Means Clustering, Principal Components Analysis, and Neural Networks, all make use of probability.In short, probability cannot be avoided!If you want to do machine learning beyond just copying library code from blogs and tutorials, you must know probability.This course will cover everything that you'd learn (and maybe a bit more) in an undergraduate-level probability class. This includes random variables and random vectors, discrete and continuous probability distributions, functions of random variables, multivariate distributions, expectation, generating functions, the law of large numbers, and the central limit theorem.Most important theorems will be derived from scratch. Don't worry, as long as you meet the prerequisites, they won't be difficult to understand. This will ensure you have the strongest foundation possible in this subject. No more memorizing "rules" only to apply them incorrectly / inappropriately in the future! This course will provide you with

Common scenario: You try to get into machine learning and data science, but there's SO&nbsp;MUCH&nbsp;MATH.Either you never studied this math, or you studied it so long ago you've forgotten it all.What do you do?Well my friends, that is why I created this course.Calculus is one of the most important math prerequisites for machine learning. It's required to understand probability and statistics, which form the foundation of data science. Backpropagation, the learning algorithm behind deep learning and neural networks, is really just calculus with a fancy name.If you want to do machine learning beyond just copying library code from blogs and tutorials, you must know calculus.Normally, calculus is split into 3 courses, which takes about 1.5 years to complete.Luckily, I've refined these teachings into just the essentials, so that you can learn everything you need to know on the scale of hours instead of years.This course will cover Calculus 1 (limits, derivatives, and the most important derivative rules), Calculus 2 (integration), and Calculus 3 (vector calculus). It will even include machine learning-focused material you wouldn't normally see in a regular college course. We will even demonstrate many of the concepts in this course using the Python programming language (don't worry, you don't need to know Python for this course). In other words, instead of the dry old college version of calculus, this course takes just the most practical and impactful topics, and provides you with skills directly applicable to machine learning and data science, so you can start applying them today.Are you ready?Let's go!Suggested prerequisites:Firm understanding of high school math (functions, algebra, trigonometry)

A-Z™ | TensorFlow ile Derin Öğrenme Kursumuzda klasik ve derin öğrenme tabanlı yöntemlerini kullanarak sınıflandırma nasıl yapıldığını öğrenip, TensorFlow kütüphaneleriyle gerçek hayat projeleri yapacağız.Projelerle Yapay Zeka ve Bilgisayarlı Görü Kursu İçeriği Giriş BölümüDerin Öğrenme Teori Derin Öğrenme Nedir Yapay Sinir AğlarıAktivasyon FonksiyonlarıOptimizasyon AlgoritmalarıLoss (Kayıp) FonksiyonlarıDerin Öğrenme TeoriCNN (Convolutional Neural Networks) Teori Evrişim İşlemiCNN (Convolutional Neural Networks)Piksel Ekleme (Padding)Adım Kaydırma (Stride)Ortaklama (Pooling)Ek Teori Epoch ve Batch Size Dropout Early Stopping Learning Rate TensorFlow ile Derin Öğrenme TensorFlow Temelleri Veriyi Hazırlama Model Oluşumu Sequential Model Egitimi Model Testi | 1. Kısım Model Testi | 2. Kısım Modeli Kaydetme/Yükleme - Save/Load Model Sonuçlarını Görselleştirme Modelin Ara Katmalarını Görselleştirme Functional Bir Model Oluşturma Callbacks | 1. kısım Callbacks | 2. kısım Data Augmentation - Veri Arttırma | 1. Kısım Data Augmentation - Veri Arttırma | 2. Kısım Transfer Learning - VGG Hazır Model Kullanma - VGG TensorFlow ile Trafik İşaretlerini Sınıflandırma Veriyi Hazırlama Model Eğitimi ve Test Real Time'da Test TensorFlow'da Weights &amp; Biases (WandB) | Özel Veri Wandb ile Keras'da Temel Fonsiyonlar Wandb ile Keras'da Sweepler Wandb ile Keras'da Sweep - Bonus Video TensorFlow Lite - Android App - Object detection - İmage Classification Efficient Det Lite Model Eğitimi - Object detection Efficient Det Lite Modeli Android'de Çalıştırma 1 - Object detection Efficient Det Lite Modeli Androi

In this training programme, you will learn Data Science and Machine Learning using Python &amp; R. It will prepare students of any discipline to find lucrative jobs in the vast field of Data Science. Students will also learn Python and R in the process. Data Science is all about processing data received from various sources and deriving information and knowledge from that. This field uses statistics and machine learning tools. Applications are Market analysis, Predictive analytics, Demand Forecast, Recommender Systems, Social Media Analysis, Person analysis etc.

Questo corso sul Data Science con R nasce per essere un percorso completo su come si è evoluta l'analisi dati negli ultimi anni a partire dall'algebra e dalla statistica classiche. L'obiettivo è accompagnare uno studente che ha qualche base di R in un percorso attraverso le varie anime del Data Science.Cominceremo con un ripasso delle basi di R, a partire dallo scaricamento e installazione, all'impostazione dell'ambiente di lavoro, passando per le strutture, la creazione di funzioni, l'uso degli operatori e di alcune funzioni importanti.&nbsp;Passeremo poi a vedere come manipolare e gestire un dataset, estrarne dei casi oppure delle variabili, generare dei dataset casuali, calcolare delle misure statistiche di base, creare grafici con i pacchetti Matplotlib e Seaborn.Nelle sezioni successive cominciamo a entrare nel cuore del Data Science con R, a cominciare dal preprocessing: vediamo infatti come ripulire e normalizzare un dataset, e come gestire i dati mancanti.&nbsp;La sezione successiva ci permette di cominciare a impostare dei modelli di machine learning con Python: vedremo tutti gli algoritmi più comuni, sia supervisionati che non supervisionati, come la regressione, semplice, multipla e logistica, il k-nearest neighbors, il Support Vector Machines, il Naive Bayes, gli alberi di decisione e il clustering.&nbsp;Passeremo poi ai più comuni metodi ensemble, come il Random Forest, il Bagging e il Boosting, e all'analisi del linguaggio naturale e al suo utilizzo nel machine learning per la catalogazione dei testi.Nelle ultime sezioni vedremo alcuni rudimenti di analisi temporale, sistemi di raccomandazione e social media mining.

¿Por qué estudiar ciencia de datos?Las empresas tienen un problema: recolectan y guardan enormes cantidades de datos en su día a día. El problema es que no tienen las herramientas y capacidades para extraer conocimiento y tomar decisiones a partir de esos datos. Pero eso está cambiando. Desde hace algunos años, la demanda de científicos de datos ha crecido exponencialmente. Tanto es así, que el número de personas con estas habilidades no es suficiente para cubrir todas las vacantes que hay. Una búsqueda básica en Glassdoor o Indeed te revelará por qué los salarios de los científicos de datos han crecido tanto en los últimos años.¿Por qué este curso?Casi todos los cursos que existen son demasiado teóricos o demasiado prácticos. Los cursos de universidad no suelen desarrollar las habilidades necesarias para enfrentarse a problemas de ciencia de datos desde cero, ni te enseñan a utilizar el software necesario de forma fluida. Por otra parte, muchos cursos y bootcamps online enseñan a utilizar estas técnicas sin llegar a entenderlas en profundidad, pasando por la teoría de forma superficial.Nuestro curso combina lo mejor de cada método. Por una parte, veremos de dónde surgen y por qué se utilizan estos métodos, entendiendo por qué funcionan de la forma que lo hacen. Por otra, vamos a programar estos métodos desde cero, utilizando las librerías más populares de la ciencia de datos y el machine learning en Python. Solo cuando hayas entendido exactamente cómo funciona cada algoritmo, aprenderemos a usarlos con las librerías avanzadas de Python.Contenido del curso Introducción al machine learning y a la ciencia de datos.Regresión lineal simple. Aprenderemos a estudiar la relación entre distintos fenómenos.Regresión lineal multiple. Crearemos modelos de más de una variable para estudiar el comportamiento de una variable de interés.Regresión

"Python TOTAL", el curso Best-Seller que ha enseñado Python desde cero a miles y miles, necesitaba un complemento perfecto:&nbsp;"Python TOTAL&nbsp;para Data Science y Machine Learning".¿Por qué hacía falta?Porque con este curso, además de aprender Python desde cero, podrás llevarlo hacia la ciencia del momento:&nbsp;Data Science (o Ciencias de la Información), para poder programar herramientas capaces de procesar cantidades monumentales de información, y de generar no solo visualizaciones relevantes, informativas y atractivas, sino también predicciones a partir de los datos que disponemos.Con "Python Total para Data science &amp; Machine Learning" podrás ayudar a quienes toman decisiones a entender mejor el contexto y la realidad sobre la cual están operando, para poder ser eficaces, eficientes y acertivos en sus decisiones.¿Que encontrarás en este curso?18 días de aprendizaje intenso y práctico Cientos de ejercicios de código en la plataforma (3 por cada lección)Vientos de archivos de código descargable Proyectos díarios del mundo real para aplicar lo aprendido Decenas de bases de datos para prácticas Cuestionarios Lecciones teóricas y prácticas hechas con amor por la simplicidad¿Qué temas cubre este curso?Python básico Pandas Num PyMatplotlib Seaborn Scikit-Learn TensorFlow Machine Learning Excel y Power BI para Data Science Algoritmos de Aprendizaje Supervisado, No Supervisado y por Reforzamiento Bases de DatosAP Is Deep Learning Etica y Provacidad en Data Sciencey muchísimo más<

Fai un passo verso il futuro: AI, Machine Learning e Data Science.Sai cosa accomuna il successo dei più grandi colossi del web come Google, Amazon e Facebook ?&nbsp;L'utilizzo che hanno fatto del machine learning.Il machine learning è la branca dell'intelligenza artificiale che ha lo scopo di insegnare ai computer ad apprendere&nbsp;autonomamente, senza essere esplicitamente programmati.Il machine learning non è una novità, ma è finito sotto la luce dei riflettori solo con il nuovo millennio, per due motivi:L'enorme quantità di dati oggi disponibile sul web.Il progresso della tecnologia e il crescente aumento&nbsp;della potenza di calcolo.Questi due fattori, uniti alle sue innumerevoli applicazioni commerciali,&nbsp;stanno contribuendo alla crescita vertiginosa del machine learning&nbsp;che sta trascinando con se l'intero campo dell'intelligenza artificiale.In questo corso pratico imparerai come funziona il machine learning e come utilizzarlo&nbsp;in maniera pratica, utilizzando il linguaggio Python e librerie popolari come Scikit-Learn, Pandas e Py Plot.Vuoi dare una svolta alla tua carriera ?L'esperto di machine learning è la professione del futuro e Linkedin lo conferma; secondo una loro recente ricerca il Machine Learning Engineer è la nuova figura più ricercata dalle aziende con un tasso di crescita di quasi il 1000% negli ultimi 5 anni ed è subito seguito dal Data Scientist.Al termine di questo corso avrai acquisito&nbsp;l'esperienza pratica e le intuizioni teoriche necessarie per lanciare la tua carriera in entrambe queste due nuove professioni.Vuoi fondare la tua startup nel campo dell'AI ?Il valore totale del mercato dell'intelligenza artificiale nel 2016 era di 1.3 miliardi di dollari; secondo una ricerca di un'importante&nbsp;società di analisi americana&nbsp;il suo valore per il 2025 potrebbe superare il 60 miliard

Hello there,Welcome to Python Numpy: Machine Learning &amp; Data Science Course Python numpy, Numpy python, python numpy: machine learning &amp; data science, python numpy, machine learning data science course, machine learning python, data science, python, oak academy, machine learning, python machine learning, python data science, numpy course, data science course Learn Numpy and get comfortable with Python Numpy in order to start into Data Science and Machine Learning OAK Academy offers highly-rated data science courses that will help you learn how to visualize and respond to new data, as well as develop innovative new technologies Whether you’re interested in machine learning, data mining, or data analysis, Udemy has a course for you Data science is everywhere Better data science practices are allowing corporations to cut unnecessary costs, automate computing, and analyze markets Essentially, data science is the key to getting ahead in a competitive global climate Python Numpy, Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective, friendly instruction for students of all levels Whether you work in machine learning or finance, or are pursuing a career in web development or data science, Python is one of the most important skills you can learn Python's simple syntax is especially suited for desktop, web, and business applications Python's design philosophy emphasizes readability and usability Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization The core programming language is quite small and the standard library is also large In fact, Python's large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasks Are you re

Selamat datang di program Pelatihan Data Science dan Machine Learning Dengan R!Pelatihan ini diperuntukan untuk rekan - rekan ingin belajar data science dan machine learning dari sudut terapan dengan memanfaatkan R.Bagi rekan - rekan yang belum menguasai pemrograman R, pelatihan juga memberikan konten pemrograman dasar untuk&nbsp; Rsehingga rekan - rekan dapat mengikuti pelatihan ini dengan baik. Bagi yang sudah bisa pemrograman R, rekan - rekan dapat melanjutkan di topik berikutnya.Seluruh konten didalam pelatihan ini dilaksanakan secara step - by - step (langkah demi langkah) dan berurutan sehingga ini diharapkan semua peserta dapat dengan mudah mengikuti semua praktikum yang diberikan didalam pelatihan ini. Diharapkan semua peserta dapat mengikuti konten pelatihan ini secara berurutan ;).Berikut ini konten yang akan diberikan pada pelatihan ini.Persiapan pelatihan Pemrograman RPengenalan tool dan editor seperti RStudio, Jupyter Notebook / Jupyter Lab, Jupyter / Notebook Dengan Anaconda, dan Google Colab Visualisasi Data Visualisasi Data dengan ggplot2Dataset, Pra-Proses dan Pengurangan Dimensi Feature Manipulasi dan Analisa data Eksplorasi data science dan machine learning Permasalahan dan Penyelesaian Kasus Linear Regression Permasalahan dan Penyelesaian Kasus Klasifikasi (Classification)Permasalahan dan Penyelesaian Kasus Kekelompokkan (Clustering)Ensemble Methods Hyperparameter Tuning Untuk Model Machine Learning Kumpulan Studi Kasus Jika ada hal - hal yang ingin ditanyakan mengenai topik diatas, rekan - rekan dapat langsung ditulisnya di ruang diskusi pada web ini sehingga rekan-rekan lainnya dapat mengetahui dan ikut terlibat diskusinya.

Cette formation est conçue pour&nbsp; donner une compréhension complète de la data science, avec un focus particulier sur l’utilisation du langage R, un des outils les plus performants pour l’analyse statistique et la visualisation de données. Voici ce que vous apprendrez au cours de cette formation :Les bases de R et de la data science : Nous commencerons par les bases du langage R, afin que vous puissiez maîtriser les outils essentiels de manipulation et d’analyse de données.Visualisation des données : L’une des compétences les plus recherchées aujourd’hui est la capacité à visualiser des données de manière claire et percutante. Nous apprendrons ensemble à utiliser des bibliothèques comme ggplot2 pour créer des graphiques informatifs.Nettoyage et préparation des données : Une partie essentielle de l’analyse de données consiste à préparer les jeux de données. Vous apprendrez à manipuler, transformer et nettoyer des données brutes pour les rendre exploitables.Algorithmes de machine learning : algorithmes d'apprentissage supervisé et non supervisé en montrant comment créer des modèles prédictifs pour résoudre des problèmes réels.Applications concrètes et projets : Tout au long de la formation, vous aurez l’occasion de travailler sur des exemples et des études de cas, afin de renforcer vos compétences et de vous préparer à intégrer le monde professionnel de la data science.

Fundamentos da linguagem de programação Python , que é a principal base de linguagem para a aplicação da ciência de dados Estudo das principais funcionalidades da biblioteca Pandas , que é a principal biblioteca de manipulação de dados da Data Science Estudo das principais funcionalidades da biblioteca Numpy , que é a principal biblioteca de manipulação de operações matemáticas Estudo das principais bibliotecas de Visualização de Dados : Matplotlib e Seaborn Manipulando Time Series, que são os tipos usados em ​​datas e horas Redução de Dimensões com PCA e TSNE Estatística para Data Science.Machine Learning , com teoria e aplicação prática de estratégias básicas e avançadas Intuição e aplicação dos seguintes modelos preditivos:Linear_Regression (Regressão Linear) Logistic_Regression (Regressão Lógica)Decision_Tree&nbsp;(Árvore de Decisão)Random_Forest (Floresta Aleatória)Stochastic_Gradient_Descent (SGD)Support_Vector_Machine (SVM) Ada Boost Gradient_Boost (Impulsionamento Gradiente)K-Means_Clustering - (K-Médias de Grupos)K-Nearest_Neighbors (KNN) PROJETO: Predição da Idade dos Passageiros do Titanic (Regressão Linear)PROJETO: Classificação de sobrevivência dos passageiros do Titanic (Classificação)PROJETO: Análise de Sentimentos de Frases do Twitter (Processamento de Linguagem Natural - PLN)PROJETO: Funcionamento e uso do modelo de detecção e classificação de objetos em imagens e vídeos YOLO (Visão Computacional)PROJETO:&nbsp;Segregando Clientes por Padrões de

¡Hola a todos y bienvenidos a este curso sobre los fundamentos del Machine Learning y su aplicación en la solución de problemas reales mediante el uso de Python 3! Mi nombre es Santiago Hernández y voy a ser vuestro instructor a lo largo de este programa formativo, tenéis más información sobre mí en la biografía o en el vídeo “Presentación del instructor”.A lo largo de este curso sobre Machine Learning y Data Science presentaré, desde un nivel muy básico y al alcance de todo tipo de perfiles, los fundamentos teóricos y matemáticos que se necesitan para comprender en detalle el funcionamiento de los algoritmos de aprendizaje automático y las técnicas de ciencia de datos más importantes en la actualidad. Para ello, utilizaré el enfoque que mejores resultados me ha proporcionado al impartir este tipo de clases en diferentes universidades, un enfoque práctico, en el que veréis como se desarrollan las diferentes funciones y ecuaciones matemáticas de mi puño y letra. Representaré gráficamente todas las intuiciones matemáticas en las que se fundamenta el Machine Learning, de manera que, cualquier persona pueda comprenderlas y avanzar con las siguientes secciones. Este no es un curso para matemáticos, es un curso para todos aquellos que quieren adentrarse en el dominio del aprendizaje automático aprendiendo unas bases sólidas que le permitan solucionar problemas reales mediante la implementación en Python 3 de las principales técnicas existentes y comprender aquellos algoritmos que surjan en el futuro.A medida que vayamos construyendo y comprendiendo estos fundamentos teóricos, iremos aplicándolos a casos de uso prácticos en los que utilizaremos conjuntos de datos reales. Yo soy un firme creyente de que aquellas cosas que se aprenden de manera teórica deben saberse aplicar a casos de uso prácticos para sacarles todo el rendimiento posible, y

Datascience;&nbsp;machine learning, data science, python, statistics, statistics, r, machine learning python, deep learning, python programming, django Hello there,Welcome to “ Kaggle - Get Best Profile in Data Science &amp; Machine Learning ” course.Kaggle is Machine Learning &amp; Data Science community. Boost your CV in Data Science, Machine Learning, Python with Kaggle Kaggle, a subsidiary of Google LLC, is an online community of data scientists and machine learning practitioners. Kaggle allows users to find and publish data sets, explore and build models in a web-based data-science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries and new problems. Whether you’re a marketer, video game designer, or programmer, Oak Academy has a course to help you apply machine learning to your work. It’s hard to imagine our lives without machine learning. Predictive texting, email filtering, and virtual personal assistants like Amazon’s Alexa and the i Phone’s Siri, are all technologies that function based on machine learning algorithms and mathematical models.Data science application is an in-demand skill in many industries worldwide — including finance, transportation, education, manufacturing, human resources, and banking. Explore data science courses with Python, statistics, machine learning, and more to grow your knowledge. Get data science training if you’re into research, statistics, and analytics.Kaggle offers a no-setup, customizable, Jupyter Notebooks environment. Access free GP Us and a huge repository of community-published data &amp; code.Kaggle is

Dieser Kurs ist dein umfassender Einstieg in die Welt des Deep Learnings – mit einem klaren Fokus auf Praxis, fundierter Theorie und moderner Python-Entwicklung mit TensorFlow 2 und Keras.Statt nur Code-Schnipsel zu kopieren, lernst du wirklich zu verstehen, wie neuronale Netze funktionieren – von der mathematischen Basis bis zur Anwendung. Du wirst eigene Modelle Schritt für Schritt selbst aufbauen und trainieren, Bilddaten analysieren und sogar Texte mit KI verarbeiten.Du startest mit den Grundlagen des Machine Learning und neuronaler Netzwerke – und steigst dann tief in die wichtigsten Netzarchitekturen ein: Von klassischen Fully Connected Networks über CNNs für Bildverarbeitung bis zu RNNs/LSTMs für Zeitreihen und Texte. Dabei kommen State-of-the-art Modelle wie Res Net und Dense Net ebenfalls nicht zu kurz.Auch Natural Language Processing (NLP) ist Teil des Kurses – perfekt, um moderne KI-Anwendungen wie Chatbots oder Textklassifizierer zu entwickeln.Kursinhalte im Überblick:Einführung in Machine Learning und neuronale Netze Mathematische Grundlagen (z. B. Aktivierungsfunktionen, Backpropagation)Eigene Modelle in TensorFlow 2 und Keras entwickeln Visualisierung und Debugging mit Tensor Board Digitale Bildverarbeitung mit CNNs Moderne Architekturen: Res Net, Dense Net Sequenzmodelle: RNNs und LSTMs für zeitabhängige Daten Einstieg in Natural Language Processing (NLP) mit Keras Praxisnahe Projekte und Übungen Ziel:Werde fit im Umgang mit modernen KI-Technologien und baue deine eigenen Deep-Learning-Modelle – fundiert, praxisnah, professionell.<p