400 Best + Free Machine Learning Courses & Certification [UPDATED]
As featured on Harvard EDU, Stackify and Inc - CourseDuck identifies and rates the Best Machine Learning Courses, Tutorials, Providers and Certifications, based on 12,000+ student reviews, public mentions, recommendations, ratings and polling 5,000+ highly active StackOverFlow members. Learn more
- 1. Learn Machine Learning By Building Projects [Eduonix] - Best Paid Course
- 2. Machine Learning by Stanford [Coursera] - Best Course Overall
- 3. Machine Learning with Python by Sentdex [YouTube] - Best YouTube Tutorial
- 4. Google's Machine Learning Crash Course [Google Developers] - Best Short Course
- 5. MIT Deep Learning for Self-Driving Cars [YouTube] - Best NEW Course
- 6. Practical Deep Learning for Coders, v3 [fast.ai] - Best Practical Course
- 7. Neural Networks and Deep Learning [Coursera] - Best Advanced Course
- 8. Machine Learning & Data Science Masterclass in Python and R [Udemy]
- 9. Machine Learning [Udacity]
- 10. Machine Learning by Columbia University and edX [edX]
Learn Machine Learning By Building Projects (2020)
Machine Learning by Stanford (2011)
- Highly recommended as your first course to dive into Machine Learning.
- Although it requires hard work, the course is very accessible for beginners.
- Presented by an expert in the field of Machine Learning and online teaching.
- Well designed with simple explanations and comprehensive content.
- Focused on the logic behind Machine Learning rather than programming and maths.
- Experienced developers may consider lectures and assignments to be too basic.
- Taught in Matlab/Octave, not Python.
- Lacks practical examples.
Machine Learning with Python by Sentdex (2016)
- In-depth tutorial covering many major topics of Machine Learning.
- Great for beginners as well as intermediate level learners.
- Interesting and knowledgeable instructor with practical approach to learning.
- Requires foundational knowledge in data science and Python.
Google's Machine Learning Crash Course (2018)
- The course is taught by Google engineers and researchers, experts in the field of Machine Learning.
- Short and sweet course but with relevant curriculum for complete beginners.
- Interactive quizzes, programming and playground exercises.
- The only framework for building ML models presented in the course is TensorFlow.
- Vague explanations of Machine Learning concepts make some of the exercises too difficult for students.
MIT Deep Learning for Self-Driving Cars (2019)
- The instructor is a researcher from one of the most prestigious universities in the world.
- The concepts are presented in a clear and straight-forward manner.
- Real-world examples to help you understand how to apply the theory behind Deep Learning.
- Too many topics covered in one tutorial, only scratches the surface of each.
- Lacks interactivity which can be inconvenient for learners to easily comprehend key concepts.
Practical Deep Learning for Coders, v3 (2019)
- Experienced instructor that provides easy to understand explanations and teaches you "how-to" instead of "why".
- Top-down learning approach perfect for students that want to apply Machine Learning fast.
- Great community of fellow-learners to help you along the course.
- This course uses fastai library that can be too difficult for beginners.
- To understand the theory befind the course, further readings and additional information are necessary.
Neural Networks and Deep Learning (2017)
- Offered by deeplearning.ai, a well known provider of a world-class AI education.
- Taught in Python and Jupyter Notebook.
- Good introduction to how to build and implement neural networks.
- Easy to understand lectures with a mix of theory and practical application.
- Useful tips and insights into Deep Learning.
- Pre-written code in assignments.
- Repetitive content.
Machine Learning & Data Science Masterclass in Python and R (2019)
- Estimate the value of used cars
- Write a spam filter
- Diagnose breast cancer
What do you learn?
- Linear Regression
- Polynomial Regression
- Logistic Regression
- Naive Bayes
- Decision trees
- Random Forest
- Read in data and prepare it for your model
- With complete practical example, explained step by step
- Find the best hyper parameters for your model
- "Parameter Tuning"
- Compare models with each other:
- How the accuracy value of a model can mislead you and what you can do about it
- K-Fold Cross Validation
- Coefficient of determination
Who this course is for:
- Developers interested in Machine Learning
Machine Learning (2015)
- The course is a part of the Online Masters Degree at one of the best universities for computer science.
- Charming and entertaining instructors.
- Broad survey of the Machine Learning field.
- Unique style of teaching that will not suit everyone.
- Long and Time-consuming.
Machine Learning by Columbia University and edX (2016)
- Offered by an Ivy League research university.
- In-depth learning experience in Machine Learning.
- Talented instructor competent in transmitting knowledge.
- Good explanations of advanced topics such as Gaussian processes.
- Requires strong mathematics background.
- Lacks interactivity and practical examples.
- Does not cover neural networks.