Every Machine Learning Course on the Internet, Ranked

Machine learning is one of the hottest and most in-demand skills in tech today. Online courses have sprung up to help learners of all levels master fundamental concepts and gain practical skills. But with so many choices, it can be challenging to know where to start.

To help you navigate this educational landscape, we exhaustively researched, took and reviewed every notable machine learning course available online. We evaluated each on criteria including comprehensiveness, instruction quality, assignments and projects, and ratings from real student reviews.

Here are our rankings of the very best online courses to learn machine learning at every level, along with detailed reviews to help you find the ideal course to boost your skills and advance your career.

What is Machine Learning?

At a high level, machine learning is all about teaching computers to learn and improve at tasks on their own, without being explicitly programmed. Using statistical techniques, machine learning algorithms build models based on sample data in order to make predictions or decisions on new data.

A machine learning project usually involves these key steps:

1. Gathering a dataset
2. Preparing the data for training
3. Choosing a model
4. Training the model on the data
5. Evaluating model performance
6. Tuning parameters for optimization
7. Making predictions on new data

Some of the main types of machine learning algorithms include:

• Supervised learning – learns a function that maps input data to known output labels. This includes classification for categorical outputs and regression for continuous numerical outputs.

• Unsupervised learning – looks for hidden patterns and structures in unlabeled data. This includes clustering, dimensionality reduction, and association rule learning.

• Reinforcement learning – learns how an agent should take actions in an environment to maximize a reward signal. Used in applications like game AI and robotics.

• Deep learning – builds multi-layered artificial neural networks to perform feature extraction and transformation. Excels at complex tasks like image classification and natural language processing.

Our Pick for Best Overall Machine Learning Course

Machine Learning (Stanford University)

Coursera/Stanford‘s Machine Learning stands out as the clear best overall choice. Taught by superstar AI pioneer Andrew Ng, this course launched Coursera and helped kick off the modern MOOC movement. With stellar reviews, it remains incredibly popular and useful years later.

The 11-week course does a masterful job teaching the fundamentals of machine learning. 5 broad topics are covered in depth:

1. Supervised learning – linear and logistic regression, neural networks, SVMs
2. Unsupervised learning – k-means, PCA, anomaly detection
3. Best practices – bias/variance, regularization, evaluation
4. Large scale machine learning – stochastic and mini-batch gradient descent, map-reduce
5. Application case studies – recommender systems, photo OCR, spam filtering

Taught in Octave/MATLAB with clearly explained math and hands-on assignments, you‘ll gain strong intuitions and practical skills for applying algorithms. Ng‘s years of experience in research and industry shine through in the thoughtful instruction.

While not the most cutting-edge, it provides an invaluable foundation that will benefit any machine learning practitioner. For motivated beginners and experts alike, this is our top pick for offering the best balance of theory and practice, breadth and depth among ML courses. Audit for free or pay for a certificate.

Best Intro to Machine Learning with Python

Machine Learning A-Z: Hands-On Python & R In Data Science

For those wanting to learn machine learning using Python, the most popular programming language for the field, Machine Learning A-Z on Udemy is our top recommendation. Updated often, it provides a comprehensive overview of key concepts and algorithms with excellent coding exercises.

The 40+ hours of video content cover data preprocessing, regression, classification, clustering, association rule learning, reinforcement learning, natural language processing, deep learning, and model performance. Instructors Kirill Eremenko and Hadelin de Ponteves clearly explain theory and walk through implementation in Python.

Compared to typical MOOCs, the course dives deeper into certain algorithms like random forests, takes a more practical and hands-on approach with complete templates to download, and includes guides on topics like model deployment and maintenance. Cons are the lack of graded assignments and less math and theory than others.

Overall, it‘s a excellent choice for motivated beginners looking for a practical intro to machine learning using Python and real-world datasets. With a 4.5-star average rating over 130,000 students, this very popular course goes on sale often. It‘s a steal for usually around $10.

Best Machine Learning Specialization

Machine Learning Specialization (University of Washington)

For a step-by-step pathway to gaining job-ready machine learning skills, look no further than Coursera‘s Machine Learning Specialization in partnership with University of Washington. Consisting of 4 courses and a hands-on capstone project, this is our pick for the best end-to-end curriculum.

The specialization is more approachable than Stanford‘s course with gentler pacing and more digestible content. It still covers key topics in good depth:

1. Foundations – regression, classification, clustering, recommender systems, deep learning
2. Regression – linear and logistic, ridge, lasso, kNN, decision trees
3. Classification – linear classifiers, boosting, decision trees, precision/recall, scaling to large datasets
4. Clustering & Retrieval – k-means, mixture models, LSH, hierarchical clustering, NLP
5. Capstone – real-world ML project on a recommendation engine

Taught in Python with a lot of great coding exercises, it provides a nice balance of theory and application. The capstone in particular sets this specialization apart in having you build and deploy an end-to-end machine learning solution. Expect to spend 4-6 months with 4-9 hours per week.

While more expensive than other options with a Coursera subscription, University of Washington‘s specialization offers the most comprehensive curriculum for mastering fundamental machine learning skills employers value. It‘s very popular with stellar reviews. Financial aid is available.

Best Advanced Machine Learning Courses

Machine Learning (Carnegie Mellon University)

Carnegie Mellon University has a top machine learning program. Their graduate level courses recorded on video make for some of the best advanced learning resources available freely online.

The first course, taught by Tom Mitchell, provides a great theoretical foundation. 24 dense lectures dive deep into key concepts, techniques and applications:

• Concept learning, decision trees, neural networks, SVM, Bayesian learning
• Computational learning theory
• VC dimensions
• Reinforcement learning
• Graphical models
• Semi-supervised learning

Lecture videos are paired with readings, assignments and a midterm. The assignments in particular are very challenging. You‘ll need a strong math and computer science background. But for mathematically mature learners, this course offers an invaluable in-depth exploration of core machine learning topics.

Statistical Machine Learning (Carnegie Mellon University)

The follow-up to Mitchell‘s course, Statistical Machine Learning taught by Larry Wasserman takes things to the next level. Again offered as a series of dense video lectures, it focuses on advanced machine learning theory and methods. Topics include:

• Estimation theory
• PAC learning
• Vapnik-Chervonenkis theory
• Concentration inequalities
• Empirical processes
• Re-sampling methods
• High-dimensional statistics

Not for the faint of heart, it‘s best tackled after a solid grounding in ML basics, linear algebra, probability and statistics. But for advanced learners, Wasserman‘s brilliant instruction makes this a gem for a deep dive into statistical learning theory. Start with Mitchell‘s course as a prerequisite.

Best Machine Learning Courses for Specific Topics

Outside of broad overview courses, there are also great options for going deep on specific machine learning topics. Some of our top picks by category:

Best for Unsupervised Learning

Cluster Analysis and Unsupervised Machine Learning in Python (Udemy)

Best for Natural Language Processing

Natural Language Processing Specialization (deeplearning.ai/Coursera)

Best for Time Series Forecasting

Time Series Forecasting (Facebook/Udacity)

Best for Reinforcement Learning

Reinforcement Learning Specialization (University of Alberta/Coursera)

Honorable Mentions

Here are a few other courses worth checking out:

• Principles of Machine Learning (Microsoft/edX) – introduces ML concepts, algorithms and data science tools like Python, R, Azure ML. Part of the Microsoft Professional Program in Data Science.

• Introduction to Machine Learning for Coders (fast.ai) – top-down, code-first approach in Python designed to quickly get you building real-world ML solutions. Taught by Rachel Thomas and Jeremy Howard.

• Intro to Machine Learning (Kaggle) – nice interactive intro with coding exercises and real datasets on the popular Kaggle data science platform. Free.

• Machine Learning Crash Course (Google) – Google‘s fast-paced intro to ML with TensorFlow APIs. Features 25 lessons and 40+ exercises. Free.

Wrapping Up

Machine learning is an exciting, fast-growing field powering many applications we use daily. While it may seem complex, the resources above offer a variety of ways to get started no matter your experience level.

Whether you prefer a hands-on or theoretical approach, are just starting out or have advanced math and programming knowledge, you can find a course to expand your machine learning skill set. Our top recommendations:

• Best Overall – Machine Learning (Stanford/Coursera)
• Best Intro with Python – Machine Learning A-Z (Udemy)
• Best Specialization – Machine Learning (Univ. Washington/Coursera)
• Best Advanced Courses – Machine Learning and Statistical Machine Learning (Carnegie Mellon)

Additional topics to explore include deep learning, data mining, and machine learning engineering, deployment and systems design. Be sure to complement your learning by practicing on real-world projects and participating in Kaggle competitions. With dedication and hard work, these courses will set you on a path to becoming a machine learning expert.