Best Machine Learning Courses on Udemy

Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024]

This course has been designed by a Data Scientist and a Machine Learning expert so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.

Over 1 Million students world-wide trust this course.

We will walk you step-by-step into the World of Machine 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 can be completed by either doing either the Python tutorials, or R tutorials, or both – Python & R. Pick the programming language that you need for your career.

This course is fun and exciting, and at the same time, we dive deep into Machine Learning. It is structured the following way:

• Part 1 – Data Preprocessing
• Part 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
• Part 3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
• Part 4 – Clustering: K-Means, Hierarchical Clustering
• Part 5 – Association Rule Learning: Apriori, Eclat
• Part 6 – Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
• Part 7 – Natural Language Processing: Bag-of-words model and algorithms for NLP
• Part 8 – Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
• Part 9 – Dimensionality Reduction: PCA, LDA, Kernel PCA
• Part 10 – Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

Each section inside each part is independent. So you can either take the whole course from start to finish or you can jump right into any specific section and learn what you need for your career right now.

Moreover, the course is packed with practical exercises that are based on real-life case studies. So not only will you learn the theory, but you will also get lots of hands-on practice building your own models.

And last but not least, this course includes both Python and R code templates which you can download and use on your own projects.

Complete A.I. & Machine Learning, Data Science Bootcamp

Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, up-to-date Data Science course on Udemy (we use the latest version of Python, Tensorflow 2.0 and other libraries).

This course is focused on efficiency: never spend time on confusing, out of date, incomplete Machine Learning tutorials anymore. We are pretty confident that this is the most comprehensive and modern course you will find on the subject anywhere (bold statement, we know).

This comprehensive and project based course will introduce you to all of the modern skills of a Data Scientist and along the way, we will build many real world projects to add to your portfolio.

You will get access to all the code, workbooks and templates (Jupyter Notebooks) on Github, so that you can put them on your portfolio right away! We believe this course solves the biggest challenge to entering the Data Science and Machine Learning field: having all the necessary resources in one place and learning the latest trends and on the job skills that employers want.

The curriculum is going to be very hands on as we walk you from start to finish of becoming a professional Machine Learning and Data Science engineer. The course covers 2 tracks. If you already know programming, you can dive right in and skip the section where we teach you Python from scratch.

If you are completely new, we take you from the very beginning and actually teach you Python and how to use it in the real world for our projects.

Don’t worry, once we go through the basics like Machine Learning 101 and Python, we then get going into advanced topics like Neural Networks, Deep Learning and Transfer Learning so you can get real life practice and be ready for the real world.

Mathematical Foundations of Machine Learning

Mathematics forms the core of data science and machine learning. Thus, to be the best data scientist you can be, you must have a working understanding of the most relevant math.

Getting started in data science is easy thanks to high-level libraries like Scikit-learn and Keras. But understanding the math behind the algorithms in these libraries opens an infinite number of possibilities up to you. From identifying modeling issues to inventing new and more powerful solutions, understanding the math behind it all can dramatically increase the impact you can make over the course of your career.

Led by deep learning guru Dr. Jon Krohn, this course provides a firm grasp of the mathematics — namely linear algebra and calculus — that underlies machine learning algorithms and data science models.

Course Sections

1. Linear Algebra Data Structures
2. Tensor Operations
3. Matrix Properties
4. Eigenvectors and Eigenvalues
5. Matrix Operations for Machine Learning
6. Limits
7. Derivatives and Differentiation
8. Automatic Differentiation
9. Partial-Derivative Calculus
10. Integral Calculus

Throughout each of the sections, you’ll find plenty of hands-on assignments, Python code demos, and practical exercises to get your math game in top form!

This Mathematical Foundations of Machine Learning course is complete, but in the future, we intend on adding extra content from related subjects beyond math, namely: probability, statistics, data structures, algorithms, and optimization. Enrollment now includes free, unlimited access to all of this future course content — over 25 hours in total.

Python for Machine Learning & Data Science Masterclass

This course is designed for the student who already knows some Python and is ready to dive deeper into using those Python skills for Data Science and Machine Learning.

The typical starting salary for a data scientists can be over $150,000 dollars, and we’ve created this course to help guide students to learning a set of skills to make them extremely hirable in today’s workplace environment.

We’ll cover everything you need to know for the full data science and machine learning tech stack required at the world’s top companies. Our students have gotten jobs at McKinsey, Facebook, Amazon, Google, Apple, Asana, and other top tech companies!

Instructors have structured the course using our experience teaching both online and in-person to deliver a clear and structured approach that will guide you through understanding not just how to use data science and machine learning libraries, but why we use them.

This course is balanced between practical real world case studies and mathematical theory behind the machine learning algorithms.

We cover advanced machine learning algorithms that most other courses don’t! Including advanced regularization methods and state of the art unsupervised learning methods, such as DBSCAN.

This comprehensive course is designed to be on par with Bootcamps that usually cost thousands of dollars and includes the following topics:

• Programming with Python
• NumPy with Python
• Deep dive into Pandas for Data Analysis
• Full understanding of Matplotlib Programming Library
• Deep dive into seaborn for data visualizations
• Machine Learning with SciKit Learn, including:
  • Linear Regression
  • Regularization
  • Lasso Regression
  • Ridge Regression
  • Elastic Net
  • K Nearest Neighbors
  • K Means Clustering
  • Decision Trees
  • Random Forests
  • Natural Language Processing
  • Support Vector Machines
  • Hierarchal Clustering
  • DBSCAN
  • PCA
  • Model Deployment

Machine Learning, Data Science and Generative AI with Python

Complete hands-on machine learning and GenAI tutorial with data science, Tensorflow, GPT, OpenAI, and neural networks

Designed for individuals with programming or scripting backgrounds, this course goes beyond the basics, preparing you to stand out in the competitive tech industry. Our curriculum, enriched with over 145 lectures and 20+ hours of video content, is crafted to provide hands-on experience with Python, guiding you from the fundamentals of statistics to the cutting-edge advancements in generative AI.

• Introduction to Python and basic statistics, setting a strong foundation for your journey in ML and AI.
• Deep Learning techniques, including MLPs, CNNs, and RNNs, with practical exercises in TensorFlow and Keras.
• Extensive modules on the mechanics of modern generative AI, including transformers and the OpenAI API, with hands-on projects like fine-tuning GPT, Advanced RAG, langchain, and LLM agents.
• A comprehensive overview of machine learning models beyond GenAI, including SVMs, reinforcement learning, decision trees, and more, ensuring you have a broad understanding of the field.
• Practical data science applications, such as data visualization, regression analysis, clustering, and feature engineering, empowering you to tackle real-world data challenges.
• A special section on Apache Spark, enabling you to apply these techniques to big data, analyzed on computing clusters.

Python for Data Science and Machine Learning Bootcamp

This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms!

This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!

This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy!

We’ll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning:

• Programming with Python
• NumPy with Python
• Using pandas Data Frames to solve complex tasks
• Use pandas to handle Excel Files
• Web scraping with python
• Connect Python to SQL
• Use matplotlib and seaborn for data visualizations
• Use plotly for interactive visualizations
• Machine Learning with SciKit Learn, including:
• Linear Regression
• K Nearest Neighbors
• K Means Clustering
• Decision Trees
• Random Forests
• Natural Language Processing
• Neural Nets and Deep Learning
• Support Vector Machines

AWS Certified Machine Learning Specialty 2024 – Hands On!

This certification prep course is taught by Frank Kane, who spent nine years working at Amazon itself in the field of machine learning. Frank took and passed this exam on the first try, and knows exactly what it takes for you to pass it yourself. Joining Frank in this course is Stephane Maarek, an AWS expert and popular AWS certification instructor on Udemy.

In addition to the 15-hour video course, a 30-minute quick assessment practice exam is included that consists of the same topics and style as the real exam. You’ll also get four hands-on labs that allow you to practice what you’ve learned, and gain valuable experience in model tuning, feature engineering, and data engineering.

This course is structured into the four domains tested by this exam: data engineering, exploratory data analysis, modeling, and machine learning implementation and operations. Just some of the topics we’ll cover include:

• How generative AI and large language models (LLM’s) work, including the Transformer architecture (GPT) and attention-based neural networks (masked self-attention)
• Amazon’s newest generative AI services: Bedrock, SageMaker JumpStart for Generative AI, CodeWhisperer, and SageMaker Foundation Models
• S3 data lakes
• AWS Glue and Glue ETL
• Kinesis data streams, firehose, and video streams
• DynamoDB
• Data Pipelines, AWS Batch, and Step Functions
• Using scikit_learn
• Data science basics
• Athena and Quicksight
• Elastic MapReduce (EMR)
• Apache Spark and MLLib
• Feature engineering (imputation, outliers, binning, transforms, encoding, and normalization)
• Ground Truth
• Deep Learning basics
• Tuning neural networks and avoiding overfitting
• Amazon SageMaker, including SageMaker Studio, SageMaker Model Monitor, SageMaker Autopilot, and SageMaker Debugger.
• Regularization techniques
• Evaluating machine learning models (precision, recall, F1, confusion matrix, etc.)
• High-level ML services: Comprehend, Translate, Polly, Transcribe, Lex, Rekognition, and more
• Building recommender systems with Amazon Personalize
• Monitoring industrial equipment with Lookout and Monitron
• Security best practices with machine learning on AWS

As an extra bonus, this course includes early access to training content for the recently announced AWS Certified Machine Learning Engineer – Associate exam (MLA-C01). This includes in-depth coverage of Amazon Bedrock, implementing Retrieval-Augmented Generation (RAG) Systems with Bedrock Knowledge Bases, content filtering with Bedrock Guardrails, and building LLM Agents (Agentic AI) using Bedrock Agents. This isn’t just theory; a series of hands-on labs gives you practical experience with these new features.