Learning machine learning apparently looks difficult to start. You do not need to hold a Ph.D. from Stanford or MIT to work in this domain. There are few basic things that are prerequisites of machine learning.

• An understanding of college level mathematics. Specially probability, statistics and linear algebra. If you are out of touch with these, it is good to revise them before starting machine learning.
• It is nice to have familiarity with any general purpose programming language like C/C++ or Java but not necessary. Python is a language that is mostly used by machine learning community. Python is very easy to learn no matter if you have done programming before or not.

The first step in starting machine learning is to follow Machine Learning course from Coursera. It is free and is been subscribed and taken by thousands of people around the globe. It is tought by a well known instructor Andrew Ng, who is a leading researcher in field of AI and ML. This course is very detailed and covers all the basic concepts involved in Machine Learning. This is a 11 weeks course, but you are free to adapt it at your own pace. I would strongly recommend to cover this course before doing anything else. It has both theoretical and practical aspects.

Now that after taking basic Machine Learning course you are good to play around with some more stuff. Kaggle is a very famous portal that holds various competitions open to everybody from vast technology areas like Data Science, Business Analytics, Machine Learning etc. These competitions are provided by renowned companies like Netflix with real world problems to solve and put their data on Kaggle. You can participate in any competition, download their data sets and play around. A few competitions have prizes(in fact some have very huge prize money) so it’s better to try your luck.

Machine Learning is currently the most hot topic in global tech industry. After gaining basics of Machine Learning you can move to more fine grained areas where Machine Learning has shown excellent results like Computer Vision, Natural Language Processing, Speech Recognition, Language Translations etc.

Deep Learning is a collection of advanced algorithms in Machine Learning. There are various free and open courses on Deep Learning. This post contains a nice collection of different courses available on deep learning. While each course listed there is best in their own area, my personal favourite is CS231n  offered by Stanford university.

There are many open source projects which are working in Machine Learning and Deep Learning domain. You can contribute to them in any way possible in order to increase your knowledge. Follow influential people in AI on Twitter to get to know about most up to date advancements in AI and ML.

If you are eager to learn ML, now is the best time to start and here is my favourite quote from Richard Branson:

“You don’t learn to walk by following rules. You learn by doing, and by falling over.”