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30 Best + Free Machine Learning Courses & Certification [2020][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

💻 Which Machine Learning Course Provider is best for me?
  • 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.
💼 What is Machine Learning used for?
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
💰 How much do Machine Learning developers make?
$108,000 - $115,999
12% of jobs
$116,000 - $123,999
7% of jobs
$129,000 is the 25th percentile. Salaries below this are outliers.
$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.
$188,000 - $196,000
17% of jobs
US National Average$108,000 $196,000$157,676/year
📃 Is a Machine Learning Certificate worth it?
Yes and No. Certified Machine Learning developers on average make more money. Having a Machine Learning certificate greatly increases the chance of landing an interview and can open otherwise closed doors. Coursera, Udacity and EdX offer excellent certificate options for impressing your future employers. Eduonix, Udemy and several other providers offer certificates, but they aren't as reputable. If you have a Computer Science Degree, certificates are not as important. Still, many employers won't care about certificates, but rather your interview skills, experience and/or skills assessment.

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399 Filtered Courses
Machine Learning by Stanford
provider
Best Course Overall

1 )

Machine Learning by Stanford (2011)

4.9
Created by the co-founder of Coursera, this course will provide you with a broad introduction to Machine Learning. It is the #1 highest rated Machine Learning course on Coursera and an excellent choice for beginners with no programming experience.
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Pros
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Cons
    • 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.
Best YouTube Tutorial

3 )

Machine Learning with Python by Sentdex (2016)

4.6
Comprehensive Machine Learning series covering everything from linear regression to neural networks provided by a famous YouTube instructor, Sentdex. This tutorial features 72 videos, and it's ideal for learners that have a basic understanding of Python.
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Pros
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Cons
    • 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
provider
Editor's Choice

4 )

Python for Data Science and Machine Learning Bootcamp (2020)

4.6
Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more!

iconWhat 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
Best Practical Course

5 )

Practical Deep Learning for Coders, v3 (2019)

4.3
Text-based and video-based introductory Machine Learning course taught by an experienced instructor and Kaggle's #1 competitor. Using PyTorch and fastai library, this tutorial is focused on practical results rather than theory.
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Pros
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Cons
    • 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
provider
Best NEW Course

6 )

Learn Machine Learning By Building Projects (2020)

5.0
Learn to build real world machine learning solutions across different verticals. Master professional machine learning.

iconWhat You'll Learn

  • Machine learning
  • Python
  • 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
provider
Best Advanced Course

7 )

Neural Networks and Deep Learning (2017)

4.8
Learn how to build and implement your own deep neural networks in just 7 hours. Taught by an experienced instructor, this is the first course in the Deep Learning Specialization.
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Pros
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Cons
    • 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.
Best Short Course

8 )

Google's Machine Learning Crash Course (2018)

3.8
Taught by Google experts, this free, concise, and highly interactive course will give you a basic understanding of Machine Learning concepts. Learn and practice at your own pace, using TensorFlow APIs.
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Pros
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Cons
    • 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.

9 )

Machine Learning (2015)

4.7
Learn Supervised, Unsupervised and Reinforcement Learning approaches from entertaining and competent instructors. Offered at Georgia Tech, this free and interactive course covers an interesting area of Artificial Intelligence.
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Pros
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Cons
    • 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 with Python by IBM
provider

10 )

Machine Learning with Python by IBM (2018)

4.7
This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components:First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms. In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed!By just putting in a few hours a week for the next few weeks, this is what you'll get. 1) New skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy 2) New projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more.3) And a certificate in machine learning to prove your competency, and share it anywhere you like online or offline, such as LinkedIn profiles and social media.If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge upon successful completion of the course.

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Overall Score : 96 / 100

11 )

MIT Deep Learning for Self-Driving Cars (2019)

4.1
Learn Deep Learning from a research scientist at MIT, one the world's most reputable universities. Great collection of courses and lectures, providing informative content and real-world examples.
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Pros
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Cons
    • 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.

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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!