25 Best + Free Machine Learning Courses & Certificates 
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
- Udemy and Eduonix are best for practical, low cost and high quality Machine Learning courses.
- Coursera, Udacity and EdX are the best providers for a Machine Learning certificate, as many come from top Ivy League Universities.
- YouTube is best for free Machine Learning crash courses.
- PluralSight, SkillShare and LinkedIn are the best monthly subscription platforms if you want to take multiple Machine Learning courses.
- Independent Providers for Machine Learning courses & certificates are generally hit or miss.
12% of jobs $116,000 - $123,999
7% of jobs $129,000 is the 25th percentile. Salaries below this are outliers.
$124,000 - $131,999
10% of jobs $132,000 - $139,999
11% of jobs $140,000 - $147,999
10% of jobs $148,000 - $155,999
1% of jobs The average salary is $157,676 a year
$156,000 - $163,999
0% of jobs $164,000 - $171,999
0% of jobs $172,000 - $179,999
10% of jobs $187,500 is the 75th percentile. Salaries above this are outliers.
$180,000 - $187,999
17% of jobs $188,000 - $196,000
17% of jobs
- 1. Machine Learning by Stanford [Coursera] - Best Course Overall
- 2. Machine Learning A-Z: Hands-On Python & R In Data Science [Udemy] - Best Paid Course
- 3. Machine Learning with Python by Sentdex [YouTube] - Best YouTube Tutorial
- 4. Python for Data Science and Machine Learning Bootcamp [Udemy] - Editor's Choice
- 5. Practical Deep Learning for Coders, v3 [fast.ai] - Best Practical Course
- 6. Learn Machine Learning By Building Projects [Eduonix] - Best NEW Course
- 7. Neural Networks and Deep Learning [Coursera] - Best Advanced Course
- 8. Google's Machine Learning Crash Course [Google Developers] - Best Short Course
- 9. Machine Learning [Udacity]
- 10. MIT Deep Learning for Self-Driving Cars [YouTube]
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 A-Z: Hands-On Python & R In Data Science (2022)
What You'll Learn
- Master Machine Learning on Python & R
- Have a great intuition of many Machine Learning models
- Make accurate predictions
- Make powerful analysis
- Make robust Machine Learning models
- Create strong added value to your business
- Use Machine Learning for personal purpose
- Handle specific topics like Reinforcement Learning, NLP and Deep Learning
- Handle advanced techniques like Dimensionality Reduction
- Know which Machine Learning model to choose for each type of problem
- Build an army of powerful Machine Learning models and know how to combine them to solve any problem
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.
Python for Data Science and Machine Learning Bootcamp (2022)
What You'll Learn
- Use Python for Data Science and Machine Learning
- Use Spark for Big Data Analysis
- Implement Machine Learning Algorithms
- Learn to use NumPy for Numerical Data
- Learn to use Pandas for Data Analysis
- Learn to use Matplotlib for Python Plotting
- Learn to use Seaborn for statistical plots
- Use Plotly for interactive dynamic visualizations
- Use SciKit-Learn for Machine Learning Tasks
- K-Means Clustering
- Logistic Regression
- Linear Regression
- Random Forest and Decision Trees
- Natural Language Processing and Spam Filters
- Neural Networks
- Support Vector Machines
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.
Learn Machine Learning By Building Projects (2022)
What You'll Learn
- Machine learning
- Learn core concepts of Machine Learning
- Learn about differnt types of machine learning algorithms
- Build real world projects using Supervised and Unsupervised learning algorithms
- Learn to implement neural networks
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.
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.
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.
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.
Machine Learning with Python by IBM (2018)
Overall Score : 96 / 100
By Michael Kuhlman on 2020-09-27
Testing our first review, but I do think that we've outdone ourselves on this page. Would love to hear any suggestions or your feedback though!