10 Best + Free Machine Learning Courses Collection

Here's a compilation of some of the best + free machine learning courses available online.

1. 

This is one of the top platforms that provide courses on topics that come under artificial intelligence and is created with the aim to teach the masses about AI and how to get started in the field. All the content is covered from scratch and focuses on learning by doing. There are a series of choices available for both beginners and experienced learners. So if you are serious about getting started in this area then the easiest way is to click on the first lecture. You can also take a look at an extended list with detailed reviews .

Key USPs:

• Each and every concept is covered with screenshots and hands-on examples.
• Complete guidance is provided to perform the configuration to get started with the lectures.
• Join the forum to communicate with peers and practitioners and help each other through the learning experience.
• Use the fast.ai library and train models.
• All the courses on this platform are available for free.

Duration: Self-paced

Rating: 4.5 out of 5

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2. 

This is undoubtedly the best machine learning course on the internet. Created by Andrew Ng, Co-Founder of Coursera and Professor at Stanford University, the program has been attended by more than 2,600,000 students & professionals globally, who have given it an average rating of a whopping 4.9 out of 5. One look at the testimonials and you will know why we so highly recommend it.

The topics covered in the course include supervised learning, best practices, and innovation in ML and AI, while you also get to encounter numerous case studies and applications among a host of other things. One of the best parts about the course is that you can enroll for a 7 day trial before going on to purchase the entire class. If you were to take our word for it, this is hands down the best program for the subject available online.
 

Key USPs:

• Understand parametric and non-parametric algorithms, clustering, dimensionality reduction, among other important topics.
• Gain best practices and advice from the instructor.
• Interact with your peers in a community of like-minded learners from all levels of experience.
• Real-world based case studies give you the opportunity to understand how problems are solved on a daily basis.
• The flexible deadline allows you to learn at your convenience.
• Learn to apply learning algorithms to build smart robots, understand text, audio, database mining.

Duration: 55 hours, 7 hours per week

Rating: 4.9 out of 5

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Review : Truly an exceptional class. Not often will someone with a deep proficiency in a discipline have the time or incentive to share their insights and teach to others; this class is a rare exception, and given the vital importance of machine learning to the future, I have a great appreciation and debt to Andrew Ng. - Nicholas D

3. 

One of the most renowned instructors of Deep Learning, Andrew Ng brings to you this special course developed in association with Stanford Professors and nvidia|deep learning institute as industry partners. The trainer is the Co-Founder of Coursera and has headed the Google Brain Project and Baidu AI group in the past.

In this program spread across 5 courses spanning a few weeks, he will teach you about the foundations of Deep Learning, how to build neural networks and how to build machine learning projects. Most importantly, you will get to work on real-time case studies around healthcare, music generation and natural language processing among other industry areas. More than 250,000 students have already enrolled in this program from all over the globe. In case you are interested in learning data science, you may want to have a look at .

Key USPs:

• Learn about convolutional networks, RNNs, BatchNorm, Dropout and more.
• Different techniques using which you can build models to solve real-life problems.
• Real-world case studies in fields such as healthcare, autonomous driving, sign language reading, music generation, and natural language processing are covered.
• Gain best practices and advice from industry experts and leaders.
• Complete all the assessments and assignments as per your schedule to earn the specialization completion certification.

Duration: 3 months, 11 hours per week

Rating: 4.9 out of 5

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Review : This course formed a concrete background in building multi-layers neural network from scratch. The best advantage of this course is I was able to immediately apply the knowledge I gained into real world problem like humanoid navigation towards known targets. Illustration is great in terms of mathematical explanation and coding in a step by step walk through. - Waleed E

4. 

Let us just begin by absorbing the fact that 411,800+ students have taken this course and it has an average rating of 4.5 out of 5. We consider this as one of the Best Machine Learning Course and it is developed by Kirill Eremenko, Data Scientist & Forex Systems Expert and Hadelin de Ponteves, Data Scientist.

This course will help you Master Machine Learning on Python and R, make accurate predictions, build a great intuition of many machine learning models, handle specific tools like reinforcement learning, NLP and Deep Learning. Most importantly it teaches you to choose the right model for each type of problem. Basic high school mathematics is all you are supposed to know to take up this course. With 40 hours of learning + 19 articles, we don't know what else we should say to make you check this out.

Key USPs:

• Great tutorial to get started with the topic with little or no prior experience.
• Explore complex topics such as natural language processing, reinforcement learning, deep learning among many others.
• Tons of practical exercises and quizzes to measure your grasp on the concepts covered in the lectures.
• Detailed instructions are provided to install the required software and tools.
• As a bonus, this training contains both Python and R code template that can be downloaded and used in projects.

Rating: 4.5 out of 5.

Duration: 41 Hours

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Review - Machine Learning A-Z is a great introduction to ML. A big tour through a lot of algorithms making the student more familiar with scikit-learn and few other packages. The theoretical explanation is elementary, so are the practical examples. ML-az is a right course for a beginner to get the motivation to dive deep in ML. From here you can choose where to go and, therefore, master it! In short, very introductory, no-brainer, wide coverage. A good way to start. -Denis Mariano

5. 

This Harvard University professional certification program uses motivating case studies, asks specific questions and shows you how to answer them by analyzing huge amounts of data. Throughout the classes, you will learn the R programming language, statistical concepts, and data analysis techniques simultaneously.The case studies covered include Trends in World Health and Economics, US Crime Rates, the Financial Crisis of 2007-2008, election Forecasting, Building a Baseball Team and Movie Recommendation Systems. The professor of this course is Rafael Irizarry, a Professor of Biostatistics at Harvard University.

Key USPs:

• Cover the fundamental R programming skills.
• Explore statistical concepts such as probability, inference, and modeling and apply them in practice.
• Gain experience with the tidyverse, including data visualization with ggplot2 and data wrangling with dplyr.
• Become familiar with essential tools for practicing data scientists such as Unix/Linux, git and GitHub, and RStudio.
• Implement machine learning algorithms and gain in-depth knowledge of this area with real-life case studies.

Duration: 9 courses, 2 to 8 weeks per course, 2 to 4 hours per week, per course

Rating: 4.7 out of 5

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6. 

This Udacity Nanodegree Program that will help you gain the must-have skills for all aspiring data analysts and data scientists. Explore the end to end process of investigating data through a machine learning lens. Learn to extract and identify useful features that can be used to represent your data in the best form. In addition to this, you will also go over some of the most important ML algorithms and evaluate their performance.

Key USPs:

• Interactive quizzes allow you to brush up the topics covered.
• Join the student support community to exchange ideas and clarify doubts.
• The self-paced schedules allow you to learn at your convenience.
• The content has been created in association with Kaggle and AWS
• You will learn about supervised learning, deep learning, unsupervised learning among a host of other topics
• You also get a one on one mentor, personal career coaching along with access to the student community

Duration: 3 months

Rating: 4.6 out of 5

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7. 

This micro masters program designed by Columbia University brings you a rigorous, advanced, professional and graduate-level foundational class in AI and its subfields like machine learning, neural networks and more. With a total of 4 courses in this program go over the important concepts of this topic none by one. Gain a solid foundation of the guiding principles of AI and apply the knowledge of machine learning to real-world challenges and applications. Along with this, you will also learn to design neural networks and utilize them to work on relevant problems. By the end of the program, you will gain adequate practical knowledge to enhance your portfolio, apply to relevant job profiles or go freelance.

Key USPs:

• Apply the concepts of machine learning to real-life challenges and applications.
• Thorough instructions are provided for configuring and navigating through the required software.
• Working on designing and harnessing the capabilities of the neural network.
• The program is divided into 4 courses along with relevant examples and demonstrations.
• Apply the knowledge gained in these lectures in an array of fields such as robotics, vision and physical simulations.

Duration: 4 courses, 12 weeks per course, 8 to 10 hours per week, per course

Rating: 4.5 out of 5

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8. 

This renowned academic institution offers a series of three graduate certifications in this fast-growing area of artificial intelligence. In the program for data, models, and optimization you will explore large scale problems by implementing appropriate algorithms and developing models. Apart from this, the AI course talks about the principles and techniques to develop a probabilistic model and work on ML. Lastly, the dataset mining class immerses into large repositories and helps you to master methods to extract information from various real-life sources.

Key USPs:

• All the topics are covered concisely and in-depth.
• Examples based on real challenges for better understanding.
• The suggestion of additional resources provided to supplement the learning.
• Complete the assessments, assignments and maintain a score above the cutoff to earn the certificate.

Duration: 1 to 2 years

Rating: 4.4 out of 5

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9. 

edX brings together a host of courses on machine learning from a variety of colleges across the globe. You can choose to study Data Science from Harvard, Artificial Intelligence from Columbia, Python Data Science from IBM or Data Science from Microsoft among a host of other courses. Most of these programs are free to audit, and you only need to pay if you wish to enroll for a certificate. With timings ranging from a few weeks to a few months, there's something for everyone in these courses.

Key USPs:

• Free courses for those not wanting to shell out big bucks to learn machine learning
• Explore the various topics of machine learning and artificial intelligence and gain a strong understanding
• Learn with an abundant amount of tips and tricks from the instructors
• Build complex data models, explore data classifications, regression and clustering and more.
• Numerous courses to choose from covering a range of topics from AI to Machine Learning, Deep Learning and more
• Top professors from leading universities teach you

Duration: Self-paced

Rating: 4.6 out of 5

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10. 

If you are well versed in R programming and statistics and want to build upon that skill then this is interactive course is worth a look. Firstly you will look into the applications and common problems that can be solved using this area. In addition to this, you will focus on the three basic techniques, and train and assess ML models. On completing the journey you can go for more advanced specialization.

Key USPs:

• Compare the different types of algorithms and experiment with them.
• Categorize data, build a decision tree, perform clustering and more.
• 15 Videos + 81 Exercises
• Interactive content makes the explanation simpler and learning a fun experience.
• The first module is available for a free preview.

Duration: 6 hours

Rating: 4.4 out of 5

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So those were some of the Best + Free Machine Learning Courses available online. Hope you found what you were looking for :) Wish you Happy Learning