Knowing machine learning is a good idea as it will open a whole new world of opportunities. With this knowledge in hand, companies will go out their way to hire you, as a professional who can help them with their problems. Plus, it has to do with artificial intelligence and AI is always cool.

So where can you go to learn machine? Here are 10 places to check out, including best books, courses and programs for a beginner to start learning machine:

Best Books on Machine Learning for Beginners

If you are just dipping your toes in machine learning, these 3 books should help you along on that journey:

1. Introduction to Machine Learning with Python

(view on Amazon)

This book by Tom M. Mitchel is a good introduction into the world of machine learning and through it and the case studies that Tom included; it will help you get a better grasp of it.

2. Pattern Recognition and Machine Learning

(view on Amazon)

Another good resource for students of machine learning comes from Christopher M. Bishop. This book is aimed at those who want to know more about pattern recognition and the use of statistical techniques in machine learning and has a lot of practical exercises in it.

3. Machine Learning for Hackers

(view on Amazon)

The third book we’ll include here is Machine Learning for Hackers, by Drew Conway and John Myles White and is mostly aimed at people who have at least basic knowledge in R, although beginners in machine learning should still be able to wrap their heads around this book and the lessons in it.

Best Online Courses on Machine Learning

If you need something with a bit more interactivity to learn machine learning, these three online courses are a good place to do it, according to FreeCodeCamp.

4. Machine Learning: (Andrew Ng, Stanford University via Coursera)

This course is taught by none other than the founder of Google Brain and former chief scientist at Baidu Andrew Ng. The 11-week course (2 weeks are dedicated to deep learning and neural networks) is one of the highest ranking on Coursera (4.7 stars) and is a good place to learn everything you ever wanted to know about machine learning. The course includes both free and pain options.

5. Machine Learning: (Professor John W. Paisley, Columbia University via edX)

Although somewhat new compared to Coursera’s Machine Learning, which was released in 2011, edX Machine Learning with Professor John W. Paisley is also a good online course to pay attention to. What’s more, Columbia’s Machine Learning course offers more algorithms than Stanford’s course, while also covering all aspects of the ML workflow, although it is aimed at students with a bit more advanced knowledge in this area.

The course takes 12 weeks (estimate) to complete, or 8-10 hours per week and also includes 4 programming assignments, 11 quizzes and a final exam. It’s free and you can buy a verified certificate once you complete the final.

6. Machine Learning A-Z: Hands-On Python & R in Data Science (Kiril Eremenko, Hadelin de Ponteves and the SuperDataScience Team via Udemy)

If you’re not so thrilled with math (though don’t expect to learn ML without at least some high school math background), then Machine Learning A-Z is a good choice. The course also covers the entire ML workflow, and has over 40 hours of on-demand videos that students can go through, as well as Python and R code templates that you can download for your own projects.

Best Programing Libraries to Learn ML

With some programming background, learning machine can be much easier. Here are some programming libraries that you should check out:

7. WEKA

If you’re looking for some ML algorithms for data mining tasks, WEKA 3: Data Mining Software in Java offers algorithms that you can apply directly to a dataset or that you can call from your Java code. It also includes tools for classification, pre-processing, clustering, regression, visualization and association rules.

This is an open source software under GNU General Public License.

This language naturally assumes you have knowledge in R, so it might not be as suited for others. Still, if you do, you’ll find the Machine Learning category on CRAN very useful, along with code developed by ML experts.

While the previous language requires that you have knowledge in R, for Scikit Learn, you need Python or Ruby. The library also comes with excellent documentation.

Ask Experts to Learn Machine

10. Join Machine Learning communities and ask experts

Finally, although you probably won’t be able to learn ML just from it, you should also get to know experts in Machine Learning on social media like Twitter and LinkedIn, Github and join the discussions on Reddit.

Don’t be shy; ask questions on what you don’t understand. This is often the best way to learn about something (yes, including Machine Learning). Once you learn enough, you can impart that knowledge on the next student of ML.

Hopefully, these 10 places will help you learn machine in no time.