Advanced Azure Machine Learning

Learn Transformers, explained: Understand the model behind GPT, BERT, and T5

State-of-the-Art Machine Learning Papers Implementation

A comprehensive course on Udemy that covers building, training, and deploying machine learning models using Microsoft Azure ML Studio, including no-code and Python-based approaches. It covers AutoML as a key component of the Azure ML platform.

Welcome to the ultimate ChatGPT and Python Data Science course—your golden ticket to mastering the art of data science intertwined with the latest AI technology from OpenAI.This course isn't just a learning journey—it's a transformative experience designed to elevate your skills and empower you with practical knowledge.With AI's recent evolution, many tasks can be accelerated using models like ChatGPT. We want to share how to leverage AI it for data science tasks.Embark on a journey that transcends traditional learning paths. Our curriculum is designed to challenge and inspire you through:Comprehensive Challenges: Tackle 10 concrete data science challenges, culminating in a case study that leverages our unique 365 data to address genuine machine learning problems.Real-World Applications: From preprocessing with ChatGPT to dissecting a furniture retailer's client database, explore a variety of industries and data types.Advanced Topics: Delve into retail data analysis, utilize regular expressions for comic book analysis, and develop a ChatGPT-powered movie recommendation system. Engage with such critical topics as AI ethics to combat biases and ensure data privacy.This course emphasizes practical application over theoretical knowledge, where you will:Perform dynamic sentiment analysis using a Naïve Bayes algorithm.Craft nuanced classification reports with our proprietary data.Gain hands-on experience with real datasets—preparing you to solve complex data science problems confidently.We’ll be using ChatGPT, Python, and Jupyter Notebook throughout the course, and I’ll link all the datasets, Notebooks for you to play around with on your own.I'll help you create a ChatGPT profile, but I’ll assume you're adept in Python and somewhat experienced in machine learning. Are you ready to dive into the

Are you ready to dive into the world of Machine Learning and Artificial Intelligence? This comprehensive Machine Learning & AI Bootcamp will take you from beginner to advanced, equipping you with the skills to build intelligent applications, automate decision-making, and apply AI to real-world challenges.What You Will Learn Understand Machine Learning fundamentals, including supervised and unsupervised learning.Master essential Python libraries like Num Py, Pandas, and Scikit-Learn for data analysis.Implement regression and classification models for predictive analytics.Explore ensemble learning techniques such as Random Forest and Gradient Boosting.Work with clustering algorithms for unsupervised learning.Learn dimensionality reduction techniques like PCA and t-SNE.Build Natural Language Processing (NLP) models for text analysis and chatbots.Apply Computer Vision for image recognition and object detection.Understand search and optimization techniques in AI.Develop AI-powered applications using Generative AI and Reinforcement Learning.Work on real-world projects, including AI chatbots, fraud detection, and recommendation systems.Who Is This Course For?Beginners looking to build a solid foundation in Machine Learning and AI.Developers and Data Scientists wanting to implement AI-driven solutions.Tech enthusiasts eager to explore NLP, Computer Vision, and AI automation.Business professionals seeking to leverage AI in decision-making and

Led by GP, a distinguished AI researcher with 11 Pub Med publications and a rich academic background from Cornell, UCSF, NIH, and Amherst College, this course spans the essentials of web development to the frontiers of AI technology. Dive into a learning experience with LIVE HELP available Monday to Friday, 9-5, plus additional online support.Our curriculum is in constant evolution, tailored to your feedback and the dynamic landscape of machine learning and AI. This isn't just another bootcamp; it's a bridge from foundational HTML to pioneering in Python 3, Machine Learning, TensorFlow, and beyond into Artificial Intelligence and Recurrent Neural Networks.Designed for rapid learning, we break down complex concepts into manageable steps. Starting from HTML and CSS to Bootstrap and Java Script, and advancing through Python 3 to data science, machine learning, and AI, we cover ground rapidly but solidly.Expect to delve into:Frontend web technologies: HTML, CSS, Bootstrap, Java Script, j Query Python programming essentials and advanced concepts Data Science, including Machine Learning and AI with TensorFlow Practical applications with projects in sentiment analysis, regression, clustering, and neural networks An exploration of both traditional statistics and machine learning techniques With over 170 lectures and 30+ hours of video content, this course is your most comprehensive guide to becoming a proficient Python developer and an AI specialist. You'll get lifetime access to all materials, including lecture Notebooks.This course is perfect for beginners with no prior programming experience, bootcamp graduates looking to tackle real-world projects, and intermediate Python programmers eager to master AI programming. With a 30-day money-back guarantee, there's no risk in taking the leap. Transform your career with the skills to thrive in the era of AI.

The fields of Artificial Intelligence and Machine Learning are considered the most relevant areas in Information Technology. They are responsible for using intelligent algorithms to build software and hardware that simulate human capabilities. The job market for Machine Learning is on the rise in various parts of the world, and the trend is for professionals in this field to be in even higher demand. In fact, some studies suggest that knowledge in this area will soon become a prerequisite for IT professionals.To guide you into this field, this course provides both theoretical and practical insights into the latest Artificial Intelligence techniques. This course is considered comprehensive because it covers everything from the basics to the most advanced techniques. By the end, you will have all the necessary tools to develop Artificial Intelligence solutions applicable to everyday business problems. The content is divided into seven parts: search algorithms, optimization algorithms, fuzzy logic, machine learning, neural networks and deep learning, natural language processing, and computer vision. You will learn the basic intuition of each of these topics and implement practical examples step by step. Below are some of the projects/topics that will be covered:Finding optimal routes on city maps using greedy search and A* (star) search algorithms Selection of the cheapest airline tickets and profit maximization using the following algorithms: hill climb, simulated annealing, and genetic algorithms Prediction of the tip you would give to a restaurant using fuzzy logic Classification using algorithms such as Naïve Bayes, decision trees, rules, k-NN, logistic regression, and neural networks Prediction of house prices using linear regression Clustering bank data using k-means algorithm Generation of association rules with A

Scopri l'avanguardia del linguaggio e della tecnologia con il mio Percorso Formativo Esclusivo su ChatGPT e Machine Learning!Siamo sulla soglia di una nuova era in cui l'intelligenza artificiale e il machine learning stanno ridefinendo il modo in cui interagiamo con la tecnologia. Per esempio, potrai usare ChatGPT per automatizzare la risposta alle domande dei tuoi clienti o per creare contenuti di blog in modo automatico. Sei pronto a essere al centro di questa rivoluzione? Allora, il mio corso è l'investimento perfetto per il tuo futuro!Questo corso è stato progettato per fornire una comprensione approfondita dei concetti chiave di Machine Learning e delle tecniche di Prompt Engineering, utilizzando come base le versioni disponibili al momento della creazione del corso.Sebbene la piattaforma possa subire aggiornamenti o modifiche nel tempo, i concetti e le strategie insegnati in questo corso sono progettati per essere universalmente applicabili, anche alle versioni più recenti e a quelle future. Questo ti permetterà di acquisire competenze durature e facilmente adattabili al progresso delle tecnologie IA.Immergiti nel cuore del machine learning, delle reti neurali e dei modelli di linguaggio con questo percorso formativo. Scoprirai ChatGPT, un software che utilizza uno dei modelli di linguaggio più avanzati al mondo, e imparerai a utilizzarlo per creare contenuti coinvolgenti, ottimizzare le tue tecniche di marketing e portare il tuo lavoro o i tuoi studi al livello successivo.Con il nostro corso, avrai l'opportunità di esplorare: I fondamenti del Machine Learning e le diverse tipologie di apprendimento; La struttura e il funzionamento delle reti neurali; L'architettura e l'apprendimento di ChatGPT; Le tecniche di Prompt Engineering per generare contenuti coinvolgenti; L'importan

One of the most essential aspects of Data Science or Machine Learning is Data Cleaning. In order to get the most out of the data, your data must be clean as uncleaned data can make it harder for you to train ML models. In regard to ML & Data Science, data cleaning generally filters & modifies your data making it easier for you to explore, understand and model.A good statistician or a researcher must spend at least 90% of his/her time on collecting or cleaning data for developing a hypothesis and remaining 10% on the actual manipulation of the data for analyzing or deriving the results. Despite these facts, data cleaning is not commonly discussed or taught in detail in most of the data science or ML courses. With the rise of big data & ML, now data cleaning has also become equally important.Why should you learn Data Cleaning?Improve decision making Improve the efficiency Increase productivity Remove the errors and inconsistencies from the dataset Identifying missing values Remove duplication Why should you take this course?Data Cleaning is an essential part of Data Science & AI, and it has become an equally important skill for a programmer. It’s true that you will find hundreds of online tutorials on Data Science and Artificial Intelligence but only a few of them cover data cleaning or just give the basic overview. This online guide for data cleaning includes numerous sections having over 5 hours of video which are enough to teach anyone about all its concepts from the very beginning. Enroll in this course now to learn all the concepts of Data Cleaning. This course teaches you everything including the basics of Data Cleaning, Data Reading, merging or splitting datasets, different visualization tools, locate or handling missing/absurd values and hands-on sessions whe

If you want to learn how to start building professional, career-boosting mobile apps and use Machine Learning to take things to the next level, then this course is for you. The Complete iOS Machine Learning Masterclass™ is the only course that you need for machine learning on iOS. Machine Learning is a fast-growing field that is revolutionizing many industries with tech giants like Google and IBM taking the lead. In this course, you’ll use the most cutting-edge iOS Machine Learning technology stacks to add a layer of intelligence and polish to your mobile apps. We’re approaching a new era where only apps and games that are considered “smart” will survive. (Remember how Blockbuster went bankrupt when Netflix became a giant?) Jump the curve and adopt this innovative approach; the Complete iOS Machine Learning Masterclass™ will introduce Machine Learning in a way that’s both fun and engaging. In this course, you will: Master the 3 fundamental branches of applied Machine Learning: Image & Video Processing, Text Analysis, and Speech & Language Recognition Develop an intuitive sense for using Machine Learning in your iOS apps Create 7 projects from scratch in practical code-along tutorials Find pre-trained ML models and make them ready to use in your iOS apps Create your own custom models Add Image Recognition capability to your apps Integrate Live Video Camera Stream Object Recognition to your apps Add Siri Voice speaking feature to your apps Dive deep into key frameworks such as coreML, Vision, Core Graphics, and Game Play Kit. Use Python, Keras, Caffee, TensorFlow, sci-kit learn, libsvm, Anaconda, and Spyder–even if you have zero experience Get FREE unlimited hosting for one year And more! This course is also full of practical use cases

This course is uniquely designed to be suitable for both experienced developers seeking to make that jump to Machine learning or complete beginners who don't understand machine learning and Artificial Neural Network from the ground up.In this course, we introduce a comprehensive training of multilayer perceptron neural networks or MLPs in MATLAB, in which, in addition to reviewing the theories related to MLPs neural networks, the practical implementation of this type of network in MATLAB environment is also fully covered.MATLAB offers specialized toolboxes and functions for working with Machine Learning and Artificial Neural Networks which makes it a lot easier and faster for you to develop a NN. At the end of this course, you'll be able to create a Neural Network for applications such as classification, clustering, pattern recognition, function approximation, control, prediction, and optimization.

Este curso abrange uma jornada empolgante na aplicação do ChatGPT no campo da Ciência de Dados e Machine Learning. Ao longo deste programa, você explorará a capacidade do ChatGPT como uma ferramenta valiosa na análise de dados, no pré-processamento e na construção de modelos de aprendizado de máquina sem precisar digitar uma linha sequer de código!Na primeira parte, mergulharemos nas técnicas fundamentais de análise de dados. Você aprenderá como extrair informações estatísticas cruciais de seus conjuntos de dados, lidar com valores ausentes e identificar e tratar valores atípicos (outliers). Exploraremos as relações entre as variáveis e a representação visual de dados categóricos e numéricos. Além disso, você terá a oportunidade de criar gráficos interativos, tornando a exploração de dados mais envolvente e informativa. Na segunda parte, nos aprofundaremos no campo do machine learning e você aprenderá a lidar com atributos categóricos usando técnicas como o Label Encoder e o One Hot Encoding. Abordaremos o desafio de conjuntos de dados desbalanceados e discutiremos a importância da transformação de escala. Você também ganhará experiência na divisão eficaz de bases de dados, seleção de algoritmos apropriados e métodos de avaliação. A validação cruzada, tuning de parâmetros e seleção de atributos são partes essenciais do processo de modelagem, e você terá a oportunidade de aprimorar suas habilidades nessas áreas.Ao concluir este curso, você estará equipado com habilidades avançadas em ciência de dados e machine learning, capacitado para aplicar o ChatGPT de forma eficaz em projetos do mundo real. Este programa oferece uma oportunidade única de melhorar suas habilidades analíticas e se destacar no campo da ciência de dados e do aprendizado de máquina. Prepare-se para alcançar um novo patamar em sua carreira profissional!

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 & 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

Welcome to the comprehensive course on Predictive Analysis and Machine Learning Techniques! In this course, you will embark on a journey through various aspects of predictive analysis, from fundamental concepts to advanced machine learning algorithms. Whether you're a beginner or an experienced data scientist, this course is designed to provide you with the knowledge and skills needed to tackle real-world predictive modeling challenges.Through a combination of theoretical explanations, hands-on coding exercises, and practical examples, you will gain a deep understanding of predictive analysis techniques and their applications. By the end of this course, you'll be equipped with the tools to build predictive models, evaluate their performance, and extract meaningful insights from data.Join us as we explore the fascinating world of predictive analysis and unleash the power of data to make informed decisions and drive actionable insights!Section 1: Introduction This section serves as an introduction to predictive analysis, starting with an overview of Java Netbeans. Students will understand the basics of predictive modeling and explore algorithms like random forest and extremely random forest, laying the groundwork for more advanced topics in subsequent sections.Section 2: Class Imbalance and Grid Search Here, students delve into more specialized topics within predictive analysis. They learn techniques for addressing class imbalance in datasets, a common challenge in machine learning. Additionally, they explore grid search, a method for systematically tuning hyperparameters to optimize model performance.Section 3: Adaboost Regressor The focus shifts to regression analysis with the Adaboost algorithm. Students understand how Adaboost works and apply it to predict traffic patterns, gaining practical experience in regression modeling.Section 4: Detecting Patterns with Unsupervised Learning</strong

This course is designed by an industry expert who has over 2 decades of IT industry experience including 1.5 decades of project/ program management experience, and over a decade of experience in independent study and research in the fields of Machine Learning and Data Science.The course will equip students with a solid understanding of the theory and practical skills necessary to learn machine learning models and data science.When building a high-performing ML model, it’s not just about how many algorithms you know; instead, it’s about how well you use what you already know.Throughout the course, I have used appealing visualization and animations to explain the concepts so that you understand them without any ambiguity.This course contains 9 sections:&nbsp; &nbsp; &nbsp;1. Introduction to Machine Learning&nbsp; &nbsp; &nbsp;2. Anaconda – An Overview &amp; Installation&nbsp; &nbsp; &nbsp;3. Jupyter Lab – An Overview&nbsp; &nbsp; &nbsp;4. Python – An Overview&nbsp; &nbsp; &nbsp;5. Linear Algebra – An Overview&nbsp; &nbsp; &nbsp;6. Statistics – An Overview&nbsp; &nbsp; &nbsp;7. Probability – An Overview&nbsp; &nbsp; &nbsp;8. OO Ps – An Overview&nbsp; &nbsp; &nbsp;9. Important Libraries – An Overview This course includes 20 lectures, 10 hands-on sessions, and 10 downloadable assets.By the end of this course, I am confident that you will outperform in your job interviews much better than those who have not taken this course, for sure.

Java Server Pages (JSP) is a server-side programming technology that enables the creation of dynamic, platform-independent method for building Web-based applications. JSP have access to the entire family of Java AP Is, including the JDBC API to access enterprise databases. This tutorial will teach you how to use Java Server Pages to develop your web applications in simple and easy steps.Why to Learn JSP?Java Server Pages often serve the same purpose as programs implemented using the Common Gateway Interface (CGI). But JSP offers several advantages in comparison with the CGI.Performance is significantly better because JSP allows embedding Dynamic Elements in HTML Pages itself instead of having separate CGI files.JSP are always compiled before they are processed by the server unlike CGI/Perl which requires the server to load an interpreter and the target script each time the page is requested.Java Server Pages are built on top of the Java Servlets API, so like Servlets, JSP also has access to all the powerful Enterprise Java AP Is, including JDBC, JNDI, EJB, JAXP, etc.JSP pages can be used in combination with servlets that handle the business logic, the model supported by Java servlet template engines.Finally, JSP is an integral part of Java EE, a complete platform for enterprise class applications. This means that JSP can play a part in the simplest applications to the most complex and demanding.Audience This tutorial has been prepared for the beginners to help them understand basic functionality of Java Server Pages (JSP) to develop your web applications. After completing this tutorial you will find yourself at a moderate level of expertise in using JSP from where you can take yourself to next levels.

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.

A Álgebra Linear é um dos fundamentos essenciais para quem deseja atuar com Ciência de Dados e Inteligência Artificial. Seja na manipulação de grandes conjuntos de dados, na construção de modelos preditivos ou na implementação de algoritmos de Machine Learning, a compreensão dessa área matemática é indispensável. Este curso foi estruturado para oferecer uma abordagem intuitiva e prática dos conceitos mais importantes, combinando teoria e implementações em Python para garantir que você aprenda aplicando.O curso é dividido em seis seções, cada uma abordando um aspecto fundamental da Álgebra Linear. Começamos com uma introdução aos conceitos básicos, onde explicamos a importância dessa disciplina e como ela se conecta com Data Science e Machine Learning. Aqui, são apresentados elementos como escalares, vetores, matrizes e tensores, além da instalação das bibliotecas necessárias para a programação em Python. Também exploramos a representação de dados e como os sistemas lineares são utilizados para resolver problemas matemáticos.Na segunda seção, aprofundamos o estudo dos vetores, suas propriedades e aplicações. Vetores são componentes fundamentais na manipulação de dados, na normalização de variáveis e até mesmo na definição de espaços multidimensionais usados em modelos preditivos. Você aprenderá sobre normas, vetores unitários, vetores ortogonais e ortonormais, além de visualizar essas estruturas de maneira intuitiva através de gráficos.Em seguida, exploramos as matrizes, que são amplamente utilizadas na representação de dados e no processamento de grandes volumes de informações. Conheceremos as principais propriedades das matrizes, suas normas, transposição, inversão e decomposições fundamentais para diversas aplicações. Esses conceitos são indispensáveis para o funcionamento de redes neurais, regressões lineares e técnicas de redução de dimensionalidade.A quarta seção é dedicada às operações envolvendo vetores e matrizes</st