Machine Learning is the new cool kid in the block. It has huge application from Space Exploration to Finances, Medicine and Science. It is also estimated that demand for Machine Learning experts is going to increase for the foreseeable future, with an estimated increase of about 60% this year alone. On top of that, the industry has gone through huge transformation over the last few years. Before, to be a machine learning expert, you needed to have a PHD (or some high education level), but that’s no longer the case. Due to the involvement of large tech companies like Alphabet (Google parent company), IBM, Microsoft and others, it is now easier for anyone to start a career in Machine Learning.

What is #100DaysOfMLCode?

This is a challenge issued to Machine Learning developers (experts or newbies) to dedicate at least an hour a day for the next 100 days to learning and building machine learning models. I will let Siraj Raval explain this in the YouTube video below: https://www.youtube.com/watch?v=cuQMBj1cWPo By accepting and committing to the challenge publicly, you are likely to be dedicated to the course. Also, the fact that you are not doing this alone will be a motivation to many people to stay true to the course. The challenge ends at the end of this year (2018). For more details about the challenge, visit the official github repo here.

I am new to Machine Learning

If you are new to Machine Learning, there multiple resources which you can use to learn and am going to share them below. But before you can get started, I would like to suggest that you familiarize yourself with Python or R Programming languages. This is because majority of the tools you are going to come across will work natively with Python or R. Also, learning some mathematics especially in linear algebra and matrices will not hurt. If you are looking to getting started, below is a list of learning resources to help you:

1. Machine Learning Crash Course by Google – This is a free course by Google to introduce you to concepts of Machine Learning and TensorFlow. It has been used by Google internally to train their ML engineers and comes highly recommended.
2. A Guide by Siraj Raval for Learning Machine Learning in 3 Months.
3. io – You can use this website to Learn Machine Learning concepts using a hands-on step by step interactive tutorial. You can also polish your Python programming skills using the same model if need be. I recommend this to those with minimal to little experience of Python Programming.
4. YouTube – If you want to learn through demonstrations and short videos, just head over to YouTube and search for Machine Learning tutorials. Also, I would recommend you follow Siraj Raval and TensorFlow YouTube Channel.
5. And finally, Kaggle – A great platform where you can learn Machine Learning.

I will keep updating this article for new learning resources as I come across them. Join #100DaysOfMLCode movement on twitter and together let us master Machine Learning. And remember to spread the love, don’t do this alone.