Advanced Data Science

Complete data science workflow specialization covering data cleaning, analysis, visualization, and machine learning applications.

Complete Machine Learning & Data Science Bootcamp 2025

Data Science and Machine Learning with R

The Data Science Course 2025: Complete Data Science Bootcamp

Complete Python Pandas Data Science Course

SQL for Data Science and Machine Learning

Statistics for Data Science and Business Analysis

A comprehensive, hands-on guide to Tableau for data science, covering all the essential skills for creating powerful visualizations for EDA.

This course was designed with the support of AI to provide an improved learning.Transform yourself from someone who struggles with AI buzzwords into a confident Natural Language Processing expert who understands both the foundational science and cutting-edge innovations that power today's AI revolution. This comprehensive course, developed with AI assistance, takes you on a complete journey from classical linguistics to the Transformer architecture behind ChatGPT, BERT, and every modern language model.Master the Complete NLP Pipeline From Classical Methods to Modern AI• Build rock-solid foundations with computational linguistics, morphology, and semantic analysis• Implement classic algorithms like TF-IDF, Hidden Markov Models, and Part-of-Speech tagging• Understand the revolutionary shift from RNNs to Transformers and why attention mechanisms changed everything• Decode the science behind BERT, GPT, and how RLHF makes AI assistants helpful and harmless• Navigate the ethical implications of bias in language models with practical mitigation strategies• Explore cutting-edge multimodal AI where vision meets language in models like CLIP and LLaVA• Grasp the geopolitical landscape of AI development, from data sovereignty to the global "chip war"This isn't just another coding tutorial – it's your complete guide to understanding how machines truly comprehend human language.The demand for NLP expertise has exploded by 400% over the past 3 years, with companies desperately seeking professionals who understand both the technical foundations and practical applications. While others struggle with surface-level tutorials, you'll gain deep comprehension of the underlying mechanisms that drive a $43 billion industry. The pressure to implement AI soluti

Schon mal darüber nachgedacht, wie große Sprachmodelle (LL Ms) die Welt verändern und beispiellose Chancen schaffen?"KI wird deinen Job nicht übernehmen, aber jemand, der weiß, wie man KI nutzt, könnte es tun," sagt Richard Baldwin.Bist du bereit, die Feinheiten von LL Ms zu meistern und ihr volles Potenzial für verschiedene Anwendungen zu nutzen, von Datenanalyse bis zur Erstellung von Chatbots und KI-Agenten?Dann ist dieser Kurs für dich!Tauche ein in 'LLM Mastery: OpenAI, Gemini, Claude, Llama, ChatGPT &amp; AP Is'—wo du die grundlegenden und fortgeschrittenen Konzepte von LL Ms, ihre Architekturen und praktischen Anwendungen erforschen wirst. Verändere dein Verständnis und deine Fähigkeiten, um die Führung in der KI-Revolution zu übernehmen.Dieser Kurs ist perfekt für Entwickler, Datenwissenschaftler, KI-Enthusiasten und alle, die an der Spitze der Technologie von LL Ms stehen möchten. Egal ob du neuronale Netzwerke verstehen, KI-Modelle feinabstimmen oder KI-gesteuerte Anwendungen entwickeln möchtest, dieser Kurs bietet dir alles, was du brauchst.Was dich in diesem Kurs erwartet:Umfassendes Wissen über LL Ms:Verständnis von LL Ms: Lerne über Parameter, Gewichte, Inferenz und neuronale Netze.Neuronale Netzwerke: Verstehe die Funktionsweise neuronaler Netze mit Tokens in LL Ms.Transformer-Architektur: Erforsche die Transformer-Architektur und Mixture of Experts.Feinabstimmung: Verstehe den Prozess der Feinabstimmung und die Entwicklung des Assistant-Modells</stron

In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning.Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.Google's Alpha Go program was able to beat a world champion in the strategy game go using deep reinforcement learning.Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.Google famously announced that they are now "machine learning first", meaning that machine learning is going to get a lot more attention now, and this is what's going to drive innovation in the coming years. It's embedded into all sorts of different products.Machine learning is used in many industries, like finance, online advertising, medicine, and robotics.It is a widely applicable tool that will benefit you no matter what industry you're in, and it will also open up a ton of career opportunities once you get good.Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world?In this course, we are first going to discuss the K-Nearest Neighbor algorithm. It’s extremely simple and intuitive, and it’s a great first classification algorithm to learn. After we discuss the concepts and implement it in code, we’ll look at some ways in which KNN can fail.It’s important to know both the advantages and disadvantages of each algorithm we look at.Next we’ll look at the Naive Bayes Classifier and the General Bayes Classifier. This is a very interesting algorithm to look at because it is grounded in probability.We’ll see how we can transform the Bayes Classifier into a linear and

Der Bedarf an Data-Experten wächst wesentlich schneller als das Angebot an Fachkräften. 2022 fehlten laut einer repräsentativen Bitkom-Umfrage rund 137.000 IT-Fachkräfte in Deutschland. Damit liegt der Mangel sogar noch höher als vor der Pandemie.Die Karriere im Bereich Data Science bietet nicht nur finanzielle Vorteile, sondern auch die Möglichkeit, an den herausforderndsten und faszinierendsten Aufgaben der Welt zu arbeiten. Bist du bereit, den Weg als Data Scientist einzuschlagen?&nbsp; &nbsp;"Perfektes Niveau, motivierend und verständlich/gründlich erklärt." (★★★★★ P. Fuchs)Dieser Grundlagenkurs richtet sich sowohl an Anfänger, die zum ersten Mal mit&nbsp;Data Science in Berührung kommen, als auch an Entwickler, die ihr Portfolio um Fähigkeiten in Richtung Data Science und Machine Learning ausbauen wollen!Wichtig: Unser Data Science-Kurs erfordert Grundkenntnisse der Programmierung mit Python! Falls du die Grundlagen von Python bisher noch nicht erlernt hast, solltest du zuerst einen unserer Python-Kurse durcharbeiten!Dieser umfassende Kurs ist inhaltlich vergleichbar mit anderen Data Science Bootcamps, die sonst mehrere tausend Euro kosten. Nun kannst Du all das zu einem Bruchteil der Kosten lernen. Und dank der Plattform Udemy lernst Du wann und wo Du möchtest. Mit über 100 HD Video Lektionen und den detaillierten Code Notebooks zu jeder Lektion ist dies einer der umfangreichsten deutschsprachigen Kurse für Data Science und Maschinelles Lernen (Machine Learning) auf Udemy!Wir bringen dir bei, wie man Python zur Analyse von Daten einsetzt, wie man Daten&nbsp;visualisiert und wie Python zum&nbsp;Maschinellen Lernen (Machine Learning)&nbsp;genutzt werden kann! Hier sind einige der Punkte die wir behandeln werd

If you write for your business or job, this AI&nbsp;Writing course is a must-watch for you. Don't wait too long to master the use of modern AI&nbsp;tools like ChatGPT and Google Bard. These AI tools can benefit you as a business, and more specifically as a writer.In this course, you'll learn how to use an AI&nbsp;writing companion to:Enhance Your Creativity Speed&nbsp;Up Your Workflow Write More &amp; Write Better Optimize Text for SE Oand so much more!Within the first 15 minutes of class, you will have a clear understanding of:How to sign up &amp; start prompting ChatGPT to help you write Best practices for writing prompts (prompt engineering)Why &amp; how you can use an AI writing assistant How can an AI Writing Companion help you?Generate ideas that inspire you to write Edit your writing for grammar &amp; spelling mistakes Automatically rewrite your work in a different tone or for a different target audience Condense or expand your writing Turn your writing into a different format (i.e. social media post, email blast, article)Translate your writing into another language Summarize long text into condensed notes Optimize your writing for search engines, making them keyword friendly Generate catchy headlines, subject lines, and titles for your content Write entire articles, posts, and other content for you Watch a free preview of this course to start learning, and to see if this is the right course for you.By the end of this course, you'll have a comprehensive understanding of AI&nbsp;writing tools such as ChatGPT &amp; Go

Machine Learning is one of the hottest technologies of our time! If you are new to ML and want to become a Data Scientist, you need to understand the mathematics behind ML algorithms. There is no way around it. It is an intrinsic part of the role of a Data Scientist and any recruiter or experienced professional will attest to that. The enthusiast who is interested in learning more about the magic behind Machine Learning algorithms currently faces a daunting set of prerequisites: Programming, Large Scale Data Analysis, mathematical structures associated with models and knowledge of the application itself. A common complaint of mathematics students around the world is that the topics covered seem to have little relevance to practical problems. But that is not the case with Machine Learning.This course is not designed to make you a Mathematician, but it does provide a practical approach to working with data and focuses on the key mathematical concepts that you will encounter in machine learning studies. It is designed to fill in the gaps for students who have missed these key concepts as part of their formal education, or who need to catch up after a long break from studying mathematics.Upon completing the course, students will be equipped to understand and apply mathematical concepts to analyze and develop machine learning models, including Large Language Models.

A warm welcome to the Generative AI with LL Ms, Prompting, Automation &amp; Agents course by Uplatz.Generative AI (Generative Artificial Intelligence) refers to a type of artificial intelligence that is capable of creating new content—such as text, images, audio, code, and more—rather than simply analyzing existing data. It mimics human creativity by learning from large datasets and generating outputs that resemble original, human-made content.What It Does Traditional AI systems are good at recognizing patterns or making predictions based on existing data. Generative AI goes a step further by actually producing new data that didn't exist before. For example:Writing articles or stories Creating images or artwork Composing music Writing code Designing products or layouts How It Works Generative AI typically relies on advanced machine learning techniques, especially deep learning models such as:Transformers – used in models like GPT (text) or T5Diffusion models – used in image generation (like DALL·E or Stable Diffusion)GANs (Generative Adversarial Networks) – used for creating realistic mediaA simplified breakdown of the process:Training The model is trained on massive datasets (e.g., books, websites, images, code).It learns statistical patterns, styles, and relationships in the data.Learning Probabilities Instead of memorizing, the model learns the probability of what should come next in a sequence (next word, next pixel, etc.).Generation (Inference)<

Welcome to this course on Machine Learning and Data Science with AWS. Amazon Web services or AWS is one of the biggest cloud computing platform where everything gets deployed to scale and action. Understanding the concepts and methods are vital, but being able to develop and deploy those concepts in forms of real life applications is something that is most weighted by the industry. Thus, here in this course, we are focused on ways you can use various cloud services on AWS to actually build and deploy you ideas into actions on multiple domains on Machine Learning and Data Science. You could be an IT&nbsp;professional looking for job change or upgrading your skillset or you could be a passionate learner or cloud certification aspirant, this course is for wider audience that if formed by the people who would like to learn any of these or a combination of these things-Create and Analyze dataset to find insights and spot outliers or trends Build Data visualization reports and dashboards by combining various visualization charts to represent data insights Develop machine learning models for Natural Language Processing for various applications on AWS And much more.Course Structure This course consists of multiple topics that are arranged in multiple sections. In the first few sections you would learn cloud services related to Data Science and Analysis on AWS with hands on practical examples. There you would be learning about creating a crawler in Glue, Analyzing dataset using SQL in Amazon Athena. After that you would learn to prepare a dataset for creating Data Visualization charts and reports that can be used for finding critical insights from the dataset that can be used in decision making process. You will learn to create calculated fields, excluded lists and filters on AWS Quicksight, followed by some advanced charts such as Word cloud and Funnel chart.After that in Machine Learning section, you will learn

Imagina crear, en pocos días, una inteligencia artificial que detecte tumores o enseñe a una consola Atari a batir récords, sin ser experto en matemáticas. El secreto está en proyectos guiados paso a paso, esto disparará tu motivación y retención.¿Qué vas a conseguir?Dominar Deep Learning e IA con TensorFlow desde cero, usando explicaciones que cualquier principiante puede entender a la primera.Construir 10 proyectos reales: detector de tumores, diagnóstico Covid con Transfer Learning, agente Atari autónomo, detector de violencia en vídeo y más, para impresionar a reclutadores con tu portafolio de proyectos de Inteligencia Artificial.Aprender con metodología 100 % práctica, probada para multiplicar la retención hasta 15 veces frente a clases teóricas con presentaciones aburridas.¿Por qué te importa?Empresas&nbsp; buscan talento en IA más que nunca: las vacantes que piden TensorFlow crecieron un 34 % en el último año y pagan hasta un 25 % más que la media STEM. Además, la tecnología de redes neuronales ya supera a radiólogos en ciertas tareas de diagnóstico, de modo que estas habilidades abren puertas que transforman carreras y cambian vidas.Requisitos Solo Python básico y ganas de experimentar—el resto (instalación de librerías, datasets y scripts) lo instalamos juntos en el curso

PyTorch: written in Python, is grabbing the attention of all data science professionals due to its ease of use over other libraries and its use of dynamic computation graphs. PyTorch is a Deep Learning framework that is a boon for researchers and data scientists. It supports Graphic Processing Units and is a platform that provides maximum flexibility and speed. With PyTorch, you can dynamically build neural networks and easily perform advanced Artificial Intelligence tasks.This comprehensive 2-in-1 course takes a practical approach and is filled with real-world examples to help you create your own application using PyTorch! Begin with exploring PyTorch and the impact it has made on Deep Learning. Design and implement powerful neural networks to solve some impressive problems in a step-by-step manner. Build a Convolutional Neural Network (CNNs) for image recognition. Also, predict share prices with Recurrent Neural Network and Long Short-Term Memory Network (LSTMs). You’ll learn how to detect credit card fraud with autoencoders and much more! By the end of the course, you’ll conquer the world of PyTorch to build useful and effective Deep Learning models with the PyTorch Deep Learning framework with the help of real-world examples!Contents and Overview This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Deep Learning with PyTorch, covers building useful and effective deep learning models with the PyTorch Deep Learning framework. In this course, you will learn how to accomplish useful tasks using Convolutional Neural Networks to process spatial data such as images and using Recurrent Neural Networks to process sequential data such as texts. You will explore how you can make use of unlabeled data using Auto-Encoders. You will also be training a neural network to learn how to balance a pole all by itself, using Reinforcement Learning. Throughout this journey, you

Complete TensorFlow Mastery For Machine Learning &amp; Deep Learning in PythonTHIS IS A COMPLETE DATA SCIENCE TRAINING WITH TensorFlow IN PYTHON!It is a full 7-Hour Python TensorFlow Data Science Boot Camp that will help you learn statistical modelling, data visualization, machine learning and basic deep learning&nbsp; using the TensorFlow framework in Python..&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE:This course is your complete guide to practical data science using the TensorFlow framework in Python.. This means, this course covers all the aspects of practical data science with TensorFlow (Google's powerful Deep Learning framework) and if you take this course, you can do away with taking other courses or buying books on Python TensorFlow based data science. &nbsp;In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and advent of TensorFlow is revolutionizing Deep Learning... By storing, filtering, managing, and manipulating data in Python and TensorFlow, you can give your company a competitive edge and boost your career to the next level.THIS&nbsp;IS&nbsp;MY&nbsp;PROMISE&nbsp;TO&nbsp;YOU: COMPLETE&nbsp;THIS&nbsp;ONE&nbsp;COURSE&nbsp;&amp;&nbsp;BECOME&nbsp;A&nbsp;PRO&nbsp;IN&nbsp;PRACTICAL&nbsp;PYTHON&nbsp;TensorFlow BASED&nbsp;DATA&nbsp;SCIENCE!But first things first.&nbsp;My name is&nbsp;Minerva Singh&nbsp;and I&nbsp;am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a&nbsp;PhD at Cambridge University (Tropical Ecology and Conservation). I have several years&nbsp;of&nbsp;experience in analyzing real life data from different sources &nbsp;using data science related techniques and producing publications for international peer reviewed journa

Welcome to this Deep Learning Image Classification course with Py Torch2.0 in Python3. Do you want to learn how to create powerful image classification recognition systems that can identify objects with immense accuracy? if so, then this course is for you what you need! In this course, you will embark on an exciting journey into the world of deep learning and image classification. This hands-on course is designed to equip you with the knowledge and skills necessary to build and train deep neural networks for the purpose of classifying images using the PyTorch framework.We have divided this course into Chapters. In each chapter, you will be learning a new concept for training an image classification model. These are some of the topics that we will be covering in this course:Training all the models with torch.compile which was introduced recently in Pytroch2.0 as a new feature.Install Cuda and Cudnn libraires for Py Torch2.0 to use GPU. How to use Google Colab Notebook to write Python codes and execute code cell by cell.Connecting Google Colab with Google Drive to access the drive data.Master the art of data preparation as per industry standards. Data processing with torchvision library. data augmentation to generate new image classification data by using:- Resize, Cropping, Random Horizontal Flip, Random Vertical Flip, Random Rotation, and Color Jitter.Implementing data pipeline with data loader to efficiently handle large datasets.Deep dive into various model architectures such as Le Net, VGG16, Inception v3, and Res Net50.Each model is explained through a nice block diagram through layer by layer for deeper understanding.Implementing the training and Inferencing pipeline.Understanding transfer learning to train models on less data.Display the model inferencing result

Python est reconnu comme l'un des meilleurs langages de programmation pour sa flexibilité. Il fonctionne dans presque tous les domaines, du développement Web au développement d'applications financières. Cependant, ce n'est un secret pour personne que la meilleure application de Python est dans les tâches d'apprentissage automatique, d'apprentissage en profondeur et d'intelligence artificielle.Bien que Python facilite l'utilisation du Machine Learning et du Deep Learning, il sera toujours assez frustrant pour quelqu'un qui n'a aucune connaissance du fonctionnement de l'apprentissage automatique.Si vous connaissez les bases de Python et que vous avez envie d'apprendre le Deep Learning, ce cours est fait pour vous. Ce cours vous aidera à apprendre à créer des programmes qui acceptent la saisie de données et automatisent l'extraction de fonctionnalités, simplifiant ainsi les tâches du monde réel pour les humains.Il existe des centaines de ressources d'apprentissage automatique disponibles sur Internet. Cependant, vous risquez d'apprendre des leçons inutiles si vous ne filtrez pas ce que vous apprenez. Lors de la création de ce cours, nous avons tout filtré pour isoler les bases essentielles dont vous aurez besoin dans votre parcours d'apprentissage en profondeur.C'est un cours de base qui convient aussi bien aux débutants qu'aux experts. Si vous êtes à la recherche d'un cours qui commence par les bases et passe aux sujets avancés, c'est le meilleur cours pour vous.Il enseigne uniquement ce dont vous avez besoin pour vous lancer dans l'apprentissage automatique et l'apprentissage en profondeur sans fioritures. Bien que cela aide à garder le cours assez concis, il s'agit de tout ce dont vous avez besoin pour commencer avec le sujet.

Si estás buscando un curso práctico, completo y avanzado para aprender Machine Learning y Data Science con Big Data utilizando Py Spark, has venido al lugar correcto.Este curso está diseñado para aprender todo lo relacionado con el Machine Learning y Data Science en Spark como modelos de aprendizaje automático de clasificación, regresión, clustering, NLP, Pipelines y técnicas para la ingeniería de datos y preprocesamiento. También te enseñaremos a programar en Py Spark y las buenas prácticas para trabajar con Big Data, visualización de datos o analítica avanzada. Finalmente, aprenderás las últimas tecnologías que han permitido impulsar el Machine learning con Spark como ML Flow, Databricks, Spark ML o Spark Koalas.Este curso es para científicos de datos o aspirantes a científicos de datos que desean obtener capacitación práctica, con las últimas tecnologías y aplicable al mundo real en Py Spark (Python para Apache Spark)El Big Data ha revolucionado el campo del Machine Learning, permitiendo entrenar modelos sobre grandes cantidades de datos. El Machine Learning convencional con Python se ha quedado obsoleto y nuevas tecnologías como Apache Spark han cobrado gran relevancia. Este curso te enseñará todo lo que necesitas saber para posicionarte en el mercado laboral del Machine Learning y aprenderás una de las habilidades más demandadas para ingenieros de datos y científicos de datos.En este curso te enseñaremos todas las habilidades de Machine Learning con Py Spark, partiendo desde las bases hasta las funcionalidades más avanzadas. Para ello utilizaremos presentaciones visu

Avec l'avènement des intelligences artificielles comme ChatGPT et Midjourney, nous vivons une véritable révolution dans le monde de la technologie. Et il est devenu indispensable de posséder des compétences en intelligence artificielle pour rester compétitif sur le marché de l'emploi. Si vous cherchez à développer vos compétences en IA, ce cours est exactement ce dont vous avez besoin pour acquérir les bases nécessaires et vous positionner comme un expert dans ce domaine en pleine croissance.Pourquoi Le deep learning avec TensorFlow et non PyTorch ?Parce que :TensorFlow a été créé par Google en 2015, tandis que PyTorch est apparu en 2017. TensorFlow a donc été utilisé et testé plus longtemps dans des applications de production.TensorFlow est plus adapté aux projets de grande envergure. TensorFlow a été conçu pour être utilisé sur des clusters de machines, ce qui en fait un choix plus approprié pour les projets de grande envergure.TensorFlow offre une grande flexibilité en termes de déploiement. TensorFlow peut être utilisé pour déployer des modèles sur différents types d'appareils, y compris les ordinateurs, les serveurs, les téléphones mobiles et les dispositifs de l'internet des objets.TensorFlow dispose d'un écosystème plus large et est utilisé dans un large éventail d'applications, allant de la reconnaissance d'image et de la vision par ordinateur à la prédiction de séries temporelles et à la modélisation du langage naturel.Les bases mathématiques du Deep Learning : Pas besoin d’être un matheux Cependant, TensorFlow encapsule plusieurs concepts mathématiques de base dont la compréhension est indispensable pour bien entrainer des réseaux de neurones.C’est pourquoi nous débutons cette formation par les bases mathématiques du Deep Learning, mais de façon pratique avec du code et non des formules mathématiques.Si vous avez le niveau Lycée en Mathématique mais pense

Are you planing to build your career in Data Science in This Year?Do you the the Average Salary of a Data Scientist is $100,000/yr?Do you know over 10 Million+ New Job will be created for the Data Science Filed in Just Next 3 years??If you are a Student / a Job Holder/ a Job Seeker then it is the Right time for you to go for Data Science!Do you Ever Wonder that Data Science is the "Most Hottest" Job Globally in 2018 - 2019!Above, we just give you a very few examples why you Should move into Data Science and Test the Hot Demanding Job Market Ever Created!The Good News is That From this Hands On Data Science and Machine Learning in R course You will Learn All the Knowledge what you need to be a MASTER in Data Science.Why Data Science is a MUST HAVE for Now A Days?The Answer Why Data Science is a Must have for Now a days will take a lot of time to explain. Let's have a look into the Company name who are using Data Science and Machine Learning. Then You will get the Idea How it BOOST your Salary if you have Depth Knowledge in Data Science &amp; Machine Learning!Here we list a Very Few Companies : -Google - For Advertise Serving, Advertise Targeting, Self Driving Car, Super Computer, Google Home etc. Google use Data Science + ML + AI to Take Decision Apple: Apple Use Data Science in different places like: Siri, Face Detection etc Facebook: Data Science , Machine Learning and AI used in Graph Algorithm for Find a Friend, Photo Tagging, Advertising Targeting, Chatbot, Face Detection etcNASA: Use Data Science For different Purpose Microsoft: Amplifying human ingenuity with Data Science So From the List of the Companies you can Understand all Big Giant to Very Small

Il s'agit du cours en ligne le plus complet pour apprendre Python, la Data Science (science des données) et le Machine Learning (apprentissage automatique). Rejoignez-nous dès maintenant pour apprendre et maîtriser ces sujets !Que contient ce cours ?Bienvenue dans le cours le plus complet pour apprendre en ligne la Data Science et le Machine Learning ! Cette Master Class a été conçue pour mettre en place ce qui semble être la meilleure façon de passer de zéro à héros pour la Data Science et le Machine Learning avec Python !Ce cours est conçu pour une personne qui connaît déjà un peu le langage Python et qui est prêt à s'immerger en profondeur dans l'utilisation de ces compétences Python pour la Data Science et le Machine Learning. Le salaire de départ typique d'un data scientist peut dépasser aisément les 100 000 euros annuel, et nous avons créé ce cours pour aider à guider les apprenants vers l'apprentissage d'un ensemble de compétences qui les rendront extrêmement intéressants (et attractifs !) dans le monde du travail actuel.Nous couvrirons tout ce que vous devez savoir sur la stack tech (compétences techniques) complète de Data Science et Machine Learning requise dans les meilleures entreprises du monde. Nos étudiants ont obtenu des emplois chez Mc Kinsey, Facebook, Amazon, Google, Apple, Asana et d'autres grandes entreprises technologiques ! Nous avons structuré le cours en nous appuyant sur notre expérience de l'enseignement en ligne (et en présentiel) afin de proposer une approche claire et structurée.&nbsp;Cela vous guidera pour comprendre non seulement comment utiliser les bibliothèques populaires de Data Science et Machine Learning, mais aussi pourquoi et quand nous les utilisons. Ce cours est un équilibre parfait entre les études de cas pratiques issues du monde réel et la théorie mathématique qui se cache derrière les algorithmes de Machine Learning <strong

Deep learning is not like any other technology, but it is in many cases the only technology that can solve certain problems. We need to ensure that all people involved in the project have a common understanding of what is required, how the process works, and that we have a realistic view of what is possible with the tools at hand. To boil down all this to its core components we could consider a few important rules:create a common ground of understanding, this will ensure the right mindsetstate early how progress should be measuredcommunicate clearly how different machine learning concepts worksacknowledge and consider the inherited uncertainty, it is part of the process In order to define AI, we must first define the concept of intelligence in general. A paraphrased definition based on Wikipedia is:Intelligence can be generally described as the ability to perceive information and retain it as knowledge to be applied towards adaptive behaviors within an environment or context.While there are many different definitions of intelligence, they all essentially involve learning, understanding, and the application of the knowledge learned to achieve one or more goals.Is this course for me?By taking this course, you will gain the tools you need to continue improving yourself in the field of app development. You will be able to apply what you learned to further experience in making your own apps able to perform more.No experience necessary. ​Even if you’ve never coded before, you can take this course. ​​One of the best features is that you can watch the tutorials at any speed you want. This means you can speed up or slow down the video if you want to!When your learning to code, you often find yourself following along with a tutor without really knowing why you're doing certain things. In this course, I will demonstrate correct coding as well as mistakes I often see an

Formation Complète Data Science et Machine Learning avec Python Devenez Data Scientist et Maîtrisez l’Apprentissage Automatique avec PythonÊtes-vous prêt à acquérir les compétences les plus recherchées dans la tech et l’analyse de données ? Cette formation complète en Data Science et Machine Learning avec Python vous guidera pas à pas, même si vous partez de zéro, pour devenir un expert capable de transformer des données en décisions stratégiques.Pourquoi choisir cette formation ?Le métier de Data Scientist figure parmi les plus demandés et les mieux rémunérés. Grâce à cette formation unique, vous apprendrez à :Analyser et manipuler des données complexes avec Python.Créer des visualisations impactantes et interactives.Développer et entraîner des modèles prédictifs avancés.Maîtriser les principales bibliothèques Python en Data Science.Un programme complet et progressif Avec plus de 100 vidéos HD, des notebooks Jupyter détaillés, des exemples concrets et des exercices pratiques, vous progresserez étape par étape jusqu’à devenir autonome.Voici un aperçu de ce que vous allez maîtriser :Programmation et traitement des données Programmation avec Python orienté Data Science Manipulation des tableaux numériques avec Num PyGestion et analyse de données tabulaires avec Pandas Lecture et traitement des fichiers CSV et Excel Visualisation de données Création de graphiques professionnels avec Matplotlib Analyse exploratoire et visualisations avancées avec Seaborn Machine Learning supervisé et non supervisé avec Scikit-Lear

In the dynamic and rapidly evolving landscape of data science and machine learning, certification serves as a powerful testament to your expertise and a crucial stepping stone in your career progression. The "Data Science &amp; Machine Learning Proficiency Exam March 2025" represents a significant milestone for intermediate professionals seeking to validate their skills and solidify their position within the industry. This course is meticulously designed to provide you with the comprehensive knowledge, practical experience, and strategic insights necessary to not only pass this exam but to excel in the real-world applications of data science and machine learning.Why This Course?This course goes beyond simple memorization and rote learning. It’s a journey of deep understanding, practical application, and strategic exam preparation. We recognize that intermediate learners possess a foundational knowledge base but require targeted guidance to refine their skills and bridge the gap between theoretical understanding and practical proficiency. Therefore, this course is designed to:Provide a Structured Learning Path: The curriculum is structured to follow the exam's blueprint, ensuring that you cover all essential topics in a logical and progressive manner.Offer Real-World Relevance: We emphasize the practical application of concepts, demonstrating how data science and machine learning are used to solve real-world problems.Deliver Targeted Practice: Realistic practice exams and quizzes are designed to simulate the actual exam experience, allowing you to build confidence and identify areas for improvement.Foster Deep Understanding: In-depth explanations and detailed examples help you grasp complex concepts and develop a strong foundation in data science and machine learning.Ensure March 2025 Readiness: The course content is co

Video Learning Path OverviewA Learning Path is a specially tailored course that brings together two or more different topics that lead you to achieve an end goal. Much thought goes into the selection of the assets for a Learning Path, and this is done through a complete understanding of the requirements to achieve a goal.Deep learning is the next step to a more advanced implementation of Machine Learning. Deep Learning allows you to solve problems where traditional Machine Learning methods might perform poorly: detecting and extracting objects from images, extracting meaning from text, and predicting outcomes based on complex dependencies, to name a few.In this practical Learning Path, you will build Deep Learning applications with real-world datasets and Python. Beginning with a step by step approach, right from building your neural nets to reinforcement learning and working with different Deep Learning applications such as computer Vision and voice and image recognition, this course will be your guide in getting started with Deep Learning concepts.Moving further with simple and practical solutions provided, we will cover a whole range of practical, real-world projects that will help customers learn how to implement their skills to solve everyday problems.By the end of the course, you’ll apply Deep Learning concepts and use Python to solve challenging tasks with real-world datasets.Key Features Get started with Deep Learning and build complex models layer by layer, with increasing complexity, in no time.A hands-on guide covering common as well as not-so-common problems in deep learning using Python.Explore the practical essence of Deep Learning in a relatively short amount of time by working on practical, real-world use cases.Author Bios Radhika Datar has more than 6 years' experience in Software Development and Content Writi

Unlock the Power of Generative AI In the rapidly evolving world of artificial intelligence, the ability to communicate effectively with AI systems is becoming a critical skill. Prompt engineering is becoming an essential as it acts as the bridge between human intent and artificial intelligence, enabling us to effectively guide AI systems to produce meaningful, accurate, and relevant responses. With AI models like ChatGPT being capable of processing vast amounts of information, the quality of their output largely depends on how well prompts are crafted.This training is a hands-on course designed to empower you with the tools and techniques to craft precise, effective prompts that harness the full potential of large language models (LL Ms) like ChatGPT and Google Gemini.A Simple Framework We will explore and use a simple yet powerful framework for building highly effective prompts. The framework is based on six building blocks: instruction, context, examples, persona, format and tone. Most Practical Methods In the second part of the course, we will review the top practical prompt engineering methods that will be useful to handle more complex use cases and tasks. Join the Gen AI Revolution Ready to embark on this transformative journey? Join me as we explore the exciting world of Generative AI.

Machine learning allows you to build more powerful, more accurate and more user friendly software that can better respond and adapt.Many companies are integrating machine learning or have already done so, including the biggest Google, Facebook, Netflix, and Amazon.There are many high paying machine learning jobs.Jump into this fun and exciting course to land your next interesting and high paying job with the projects you’ll build and problems you’ll learn how to solve.In just a matter of hours you'll have new skills with projects to back them up:&nbsp; Deep dive into machine learning Problems that machine learning solves Types of machine learning Common machine learning structures Steps to building a machine learning model Build a linear regression machine learning model with TensorFlow Test and train the model Python variables and operators Collection types Conditionals and loops Functions Classes and objects Install and import Num PyBuild Num Py arrays Multidimensional Num Py arrays Array indexes and properties Num Py functions Num Py operations And much more!Add new skills to your resume in this project based course: Graph data with Py Plot Customize graphs Build 3D graphs with Py Plot Use TensorFlow to build a program to categorize irises into different species.Build a classification model Track data Implement logic Implement responsiveness Build data structures Replace Python lists with Num Py arrays Build and use Num Py arrays Use common array

Are you ready to embark on a data-driven journey into the world of machine learning and data science? If you're looking for a practical yet powerful starting point, then you're in the right place. Linear regression, the simple yet highly popular machine learning algorithm, is your gateway. It's not just jargon; it's a versatile tool used to uncover crucial insights in your data and predict the future.In this hands-on data science and machine learning project, we'll delve into the driving factors behind California house prices. You'll learn how to clean and visualize data, process it, and harness various Python libraries. By the end of this project, you'll have mastered linear regression in Python and gained essential skills for conducting data science projects.What You'll Gain:Mastery of Python Libraries: Dive into data science and machine learning with pandas, Scikit-Learn, statsmodels, matplotlib, and seaborn.Real-World Application: Apply your knowledge to a hands-on project that you can showcase on your personal website and resume.Step-by-Step Approach: Follow a clear, concise case study to build your confidence and expertise in machine learning and data science.Start your data science journey with a simple yet strong foundation. Let's get started!This course will empower you to unlock the potential of data science, equipping you with the skills to make informed decisions and drive success in the tech industry.

TensorFlow is the world’s most widely adopted framework for Machine Learning and Deep Learning. TensorFlow 2.0 is a major milestone due to its inclusion of some major changes making TensorFlow easier to learn and use such as “Eager Execution”. It will support more platforms and languages, improved compatibility and remove deprecated AP Is.This course will guide you to upgrade your skills in Machine Learning by practically applying them by building real-world Machine Learning projects.Each section should cover a specific project on a Machine Learning task and you will learn how to implement it into your system using TensorFlow 2. You will implement various Machine Learning techniques and algorithms using the TensorFlow 2 library. Each project will put your skills to test, help you understand and overcome the challenges you can face in a real-world scenario and provide some tips and tricks to help you become more efficient. Throughout the course, you will cover the new features of TensorFlow 2 such as Eager Execution. You will cover at least 3-4 projects. You will also cover some tasks such as Reinforcement Learning and Transfer Learning.By the end of the course, you will be confident to build your own Machine Learning Systems with TensorFlow 2 and will be able to add this valuable skill to your CV.About the Author Vlad Ionescu is a lecturer at Babes-Bolyai University. He has a PhD in machine learning, a field he is continuously researching and exploring every day with technologies such as Python, Keras, and TensorFlow.His philosophy is “If I can't explain something well enough for most people to understand it, I need to go back and understand it better myself before trying again”. This philosophy helps him to give of his best in his lectures and tutorials.He started as a high school computer science teacher while he was doing his Masters over 5 years ago. Right now, he teaches various university-level courses and tutorials, coverin

¿Te apetece hacer un curso diferente, en el que no solo aprenderás a dominar todos los pasos de un proyecto de Data Science, sino que también te proporcionará un montón de documentos con toda la teoría y el código que verás en las clases? ¿Te interesa tener una guía, en formato visual y también escrito? Este programa es una mezcla entre el formato de un videocurso tradicional y un máster convencional: está pensado para que, clase a clase, vayas almacenando toda una colección de recursos que, sin duda, se convertirá en tu manual de referencia. Aprenderás a estructurar un proyecto desde cero: sabrás cómo empezar y desarrollar cualquier análisis de datos y conocerás a la perfección todas las herramientas que necesitarás durante el proceso, desde simples funciones de carga de datos, hasta completas librerías de Machine Learning. Además, repasarás todos los conceptos clave de estadística y conocerás cómo funcionan los algoritmos de Machine Learning desde el punto de vista matemático, explicados de una forma gráfica y sencilla. No necesitas tener experiencia previa, ¡pero verás cómo al final del curso te conviertes en todo un experto!A día de hoy, encontrarás:Una colección de más de 30 cuadernos y archivos de Python, totalmente documentados.Documentos en PDF con copia de lo que vamos a ver en las pizarras de trabajo.Recursos y referencias útiles.Trucos, consejos y advertencias de errores que se suelen cometer.Además, tendrás acceso a todas las actualizaciones del curso y a los nuevos recursos que se vayan añadiendo, para siempre.

Este curso sobre el lenguaje de programación R está diseñado para aprender desde cero, paso a paso, hasta convertirte en un experto.Todo está explicado mediante ejemplos para facilitar el aprendizaje Estos son los temas tratados en este curso sobre RConfiguración del entorno Instalación de R y RStudio Introducción a R Operaciones aritméticas, variables, tipos de datos, vectores, operadores de comparación, ayuda y documentación Matrices en R Operaciones aritméticas con matrices, selección de elementos, selección por filas y columnas, función factor Data Frames en R Creación de Data Frames, dataset, selección y ordenación, exportar e importar datos y tratamiento de valores nulos Listas en R Creación y manejo de listas Entrada y salida de datos en R Ficheros CSV, ficheros EXCEL y bases de datos Programación básica de R Operadores lógicos, condicionales if else, bucle while, bucle for y funciones Programación avanzada de R Funciones predefinidas, funciones sobre vectores, funciones anónimas, funciones matemáticas, expresiones regulares, fecha/hora Manipulación de datos con R Manipulación de datos con dplyr, operador pipe y limpieza de datos con tidyr Visualización de datos con R Histogramas, scatterplots, barplots, boxplots, gráficos de distribución, límites y dimensiones Gráficos interactivos con Plotly Introducción a Machine Learning Machine Learning Algoritmo de regresión lineal Algoritmo de regresión logística Algoritmo de los K vecinos más cercanos Algoritmo de árboles de decisión Algo

You're looking for a complete Convolutional Neural Network (CNNs) course that teaches you everything you need to create a Image Recognition model in Python, right?You've found the right Convolutional Neural Networks course!After completing this course you will be able to:Identify the Image Recognition problems which can be solved using CNNs Models.Create CNNs models in Python using Keras and TensorFlow libraries and analyze their results.Confidently practice, discuss and understand Deep Learning concepts Have a clear understanding of Advanced Image Recognition models such as Le Net, Google Net, VGG16 etc.How this course will help you?A Verifiable Certificate of Completion is presented to all students who undertake this Convolutional Neural networks course.If you are an Analyst or an ML scientist, or a student who wants to learn and apply Deep learning in Real world image recognition problems, this course will give you a solid base for that by teaching you some of the most advanced concepts of Deep Learning and their implementation in Python without getting too Mathematical.Why should you choose this course?This course covers all the steps that one should take to create an image recognition model using Convolutional Neural Networks.Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep

This video course will get you up-and-running with one of the most cutting-edge deep learning libraries: PyTorch. Written in Python, PyTorch is grabbing the attention of all data science professionals due to its ease of use over other libraries and its use of dynamic computation graphs.In this course, you will learn how to accomplish useful tasks using Convolutional Neural Networks to process spatial data such as images and using Recurrent Neural Networks to process sequential data such as texts. You will explore how you can make use of unlabeled data using Auto Encoders. You will also be training a neural network to learn how to balance a pole all by itself, using Reinforcement Learning. Throughout this journey, you will implement various mechanisms of the PyTorch framework to do these tasks.By the end of the video course, you will have developed a good understanding of, and feeling for, the algorithms and techniques used. You'll have a good knowledge of how PyTorch works and how you can use it in to solve your daily machine learning problems.This course uses Python 3.6, and PyTorch 0.3, while not the latest version available, it provides relevant and informative content for legacy users of Python, and PyTorch.About the Author Anand Saha is a software professional with 15 years' experience in developing enterprise products and services. Back in 2007, he worked with machine learning to predict call patterns at TATA Communications. At Symantec and Veritas, he worked on various features of an enterprise backup product used by Fortune 500 companies. Along the way he nurtured his interests in Deep Learning by attending Coursera and Udacity MOO Cs.He is passionate about Deep Learning and its applications; so much so that he quit Veritas at the beginning of 2017 to focus full time on Deep Learning practices. Anand built pipelines to detect and count endangered species from aerial images, trained a robotic arm to pick and place objects, and imp

¿Te suenan términos como *Machine Learning* o *Data Scientist*? ¿Te has preguntado para qué se utilizan estas técnicas y por qué las empresas están dispuestas a pagar entre 120.000 y 200.000 dólares al año a un científico de datos?Este curso está diseñado para resolver todas tus dudas y brindarte una formación integral en Data Science. Juan Gabriel Gomila, un profesional reconocido en el campo del Data Science, te guiará a lo largo del curso, compartiendo su vasto conocimiento y ayudándote a desmitificar la teoría matemática detrás de los algoritmos de Machine Learning. Aprenderás a dominar las librerías de Python que son esenciales en esta área, convirtiéndote en un experto en la materia.A lo largo del curso, abordarás conceptos y algoritmos clave del Machine Learning, de manera progresiva y detallada. Cada sección te proporcionará nuevas habilidades que te permitirán comprender y aplicar los principios del Data Science, una disciplina no solo fascinante, sino también altamente lucrativa.Además, este curso mantiene el estilo característico y ameno de Juan Gabriel Gomila, lo que hará que disfrutes aprendiendo técnicas de Machine Learning con Python.El curso incluye ejercicios prácticos y datasets basados en ejemplos del mundo real, lo que te permitirá no solo aprender la teoría, sino también aplicarla en la creación de tus propios modelos de Machine Learning. Además, tendrás acceso a un repositorio en Git Hub con todo el código fuente en Python, listo para descargar y usar en tus proyectos.¡No esperes más! Únete a este curso y comienza a formarte en Machine Learning con el programa más completo y práctico del mercado en español.

Este taller equipa a los líderes empresariales para impulsar iniciativas de IA, y posteriormente entregar e implementar soluciones de IA, generando cambios en toda la organización con un impacto comercial medible.¡Esto es muy diferente a un “curso” tradicional!De hecho, no es un curso como tal: es un informe ejecutivo. Un briefing integral y orientado a la acción sobre IA Generativa, diseñado por líderes y para líderes.Lo que cubriremos Experiencia en IA desde una perspectiva comercial Casos de uso reales: tanto historias de éxito como fracasos Kits de herramientas accionables para aplicar en tu negocio Ejemplos desde startups en stealth mode hasta empresas globales Lo que NO cubriremos Detalles técnicos profundos (pero sí lo suficiente para apoyar la toma de decisiones).Ejemplo: abordaremos RAG, fine-tuning y agentes, pero siempre desde un punto de vista empresarial.Uso directo de herramientas de IA por parte del alumno.Este informe trata sobre cómo transformar tu organización para que use herramientas de IA, no sobre el uso individual de cada una.Si eres un ejecutivo, emprendedor o líder (o estás en el camino de convertirte en uno), este briefing te colocará en una posición estratégica para alcanzar el éxito comercial con la IA generativa.Lo que aprenderás Estrategia de IA, toma de decisiones en IA y liderazgo en IA.Este taller desarrolla tu expertise a través de 3 módulos:Módulo 1: C

Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.Welcome to PyTorch:&nbsp;Deep Learning and Artificial Intelligence!Although Google's Deep Learning library TensorFlow has gained massive popularity over the past few years, PyTorch has been the library of choice for professionals and researchers around the globe for deep learning and artificial intelligence.Is it possible that TensorFlow is popular only because Google is popular and used effective marketing?Why did TensorFlow change so significantly between version 1 and version 2?&nbsp;Was there something deeply flawed with it, and are there still potential problems?It is less well-known that PyTorch is backed by another Internet giant, Facebook (specifically, the Facebook AI&nbsp;Research Lab - FAIR). So if you want a popular deep learning library backed by billion dollar companies and lots of community support, you can't go wrong with PyTorch. And maybe it's a bonus that the library won't completely ruin all your old code when it advances to the next version. ;)On the flip side, it is very well-known that all the top AI shops (ex. OpenAI, Apple, and JP Morgan Chase) use PyTorch. OpenAI just recently switched to PyTorch in 2020, a strong sign that PyTorch is picking up steam.If you are a professional, you will quickly recognize that building and testing new ideas is extremely easy with PyTorch, while it can be pretty hard in other libraries that try to do everything for you. Oh, and it's faster.Deep Learning has been responsible for some amazing achievements recently, such as:<ul

Máster Especialista de Deep Learning en Python con PyTorch.Redes Neuronales Profundas con PyTorch: Diseño, Implementación y Evaluación de Modelos Neuronales desde 0 a experto.Instructor: PhD. Manuel Castillo-Cara Requisitos previos: Se recomienda tener conocimientos sobre Machine Learning. Se recomienda realizar previamente siguiente curso de Udemy:Machine Learning con Python. Aprendizaje Automático Avanzado - Aprendizaje Automático Scikit-Learn en Python. Modelos Predictivos. Data Science. De básico a Experto.Descripción del Curso:Bienvenido al curso de Deep Learning con Python y PyTorch. En este curso exploraremos a fondo la librería PyTorch de Python para Deep Learning, aprendiendo cómo utilizarla para desarrollar y evaluar modelos de Deep Learning avanzados. Nuestro objetivo es proporcionarte las técnicas, el código y las habilidades necesarias para que puedas aplicar el Deep Learning en tus propios proyectos innovadores.PyTorch se ha convertido en una de las herramientas más potentes y flexibles en el campo del aprendizaje profundo. A diferencia de otras librerías, PyTorch ofrece un enfoque dinámico y intuitivo para la construcción de redes neuronales, permitiéndote definir y modificar tus modelos con gran facilidad.En este curso, nos centraremos en el desarrollo práctico de modelos de Deep Learning utilizando PyTorch. Comenzaremos con los fundamentos y avanzaremos hacia técnicas más sofisticadas, permitiéndote construir una base sólida que podrás expandir en el futuro según tus necesidades y proyectos específicos.Hemos elegido PyTorch como nuestra plataforma principal debido a su capacidad para desarrollar rápidamente modelos de Deep Learning potentes y eficientes. PyTorch combina la potencia de la computación GPU con una API intuitiva, lo que nos permitir

With the increase of data by each passing day, Data Science has become one of the most important aspects in most of the fields. From healthcare to business, everywhere data is important. However, it revolves around 3 major aspects i.e. data, foundational concepts and programming languages for interpreting the data. This course teaches you everything about all the foundational mathematics for Data Science using R programming language, a language developed specifically for performing statistics, data analytics and graphical modules in a better way.Why Learn Foundational mathematical Concepts for Data Science Using R?Data Science has become an interdisciplinary field which deals with processes and systems used for extracting knowledge or making predictions from large amounts of data. Today, it has become an integral part of numerous fields resulting in the high demand of professionals of data science. From helping brands to understand their customers, solving complex IT problems, to its usability in almost every other field makes it very important for the functioning and growth of any organizations or companies. Depending upon the location the average salary of data scientist expert can be over $120,000. This course will help you learn the concepts the correct way.Why You Should Take This Online Tutorial?Despite the availability of several tutorials on data science, it is one of the online guides containing hand-picked topics on the concepts for foundational mathematics for Data Science using R programming language. It includes myriads of sections (over 9 hours of video content) lectured by Timothy Young, a veteran statistician and data scientists . It explains different concepts in one of the simplest form making the understanding of Foundational mathematics for Data Science very easy and effective.This Course includes:Overview of Machine Learning and R programming language Linear Algebra- Scalars

Data scientists, machine learning engineers, and AI researchers all have their own skillsets. But what is that one special thing they have in common?They are all masters of deep learning. We often hear about AI, or self-driving cars, or the ‘algorithmic magic’ at Google, Facebook, and Amazon. But it is not magic - it is deep learning. And more specifically, it is usually deep neural networks – the one algorithm to rule them all.Cool, that sounds like a really important skill; how do I become a Master of Deep Learning?There are two routes you can take: The unguided route – This route will get you where you want to go, eventually, but expect to get lost a few times. If you are looking at this course you’ve maybe been there. The 365 route – Consider our route as the guided tour. We will take you to all the places you need, using the paths only the most experienced tour guides know about. We have extra knowledge you won’t get from reading those information boards and we give you this knowledge in fun and easy-to-digest methods to make sure it really sticks.Clearly, you can talk the talk, but can you walk the walk? – What exactly will I get out of this course that I can’t get anywhere else?Good question! We know how interesting Deep Learning is and we love it! However, we know that the goal here is career progression, that’s why our course is business focused and gives you real world practice on how to use Deep Learning to optimize business performance.We don’t just scratch the surface either – It’s not called ‘Skin-Deep’ Learning after all. We fully explain the theory from the mathematics behind the algorithms to the state-of-the-art initialization methods, plus so much more.Theory is no good without putting it into practice, is it? That’s why we give you plenty of opportunities to put this theory to use. Implement cutting edge

Python est reconnu comme l'un des meilleurs langages de programmation pour sa flexibilité. Il fonctionne dans presque tous les domaines, du développement Web au développement d'applications financières. Cependant, ce n'est un secret pour personne que la meilleure application de Python est dans les tâches de data science, d'analyse de données et de Machine Learning.Bien que Python facilite l'utilisation du Machine Learning et de l'analyse de données, il sera toujours assez frustrant pour quelqu'un qui n'a aucune connaissance du fonctionnement de l'apprentissage automatique.Si vous avez envie d'apprendre l'analyse de données et le Machine Learning avec Python, ce cours est fait pour vous. Ce cours vous aidera à apprendre à créer des programmes qui acceptent la saisie de données et automatisent l'extraction de fonctionnalités, simplifiant ainsi les tâches du monde réel pour les humains.Il existe des centaines de ressources d'apprentissage automatique disponibles sur Internet. Cependant, vous risquez d'apprendre des leçons inutiles si vous ne filtrez pas ce que vous apprenez. Lors de la création de ce cours, nous avons tout filtré pour isoler les bases essentielles dont vous aurez besoin dans votre parcours d'apprentissage en profondeur.C'est un cours de base qui convient aussi bien aux débutants qu'aux experts. Si vous êtes à la recherche d'un cours qui commence par les bases et passe aux sujets avancés, c'est le meilleur cours pour vous.Il enseigne uniquement ce dont vous avez besoin pour vous lancer dans l'apprentissage automatique et l'analyse de données sans fioritures. Bien que cela aide à garder le cours assez concis, il s'agit de tout ce dont vous avez besoin pour commencer avec le sujet.