Oh no, not another machine learning journey
We hear many buzz words these days ranging from AI to machine learning to deep neural networks(which to most is a dark art) and to someone like me who wants to learn more about the field, It sure is a daunting task finding where to start your journey.
My father always said, if you are the smartest one in the room, then it’s time to change the room(He was not the first one to coin that term, but you get the point). You can never reach your fullest potential if you are complacent with where you are. This is why I make it a point to follow some influential people in the area of machine learning and AI with the 21st century stalking mechanisms which mostly includes Twitter and LinkedIn for me.
It is by my astute stalking mechanisms that I stumbled upon a post from one person that shared this amazing PACKT deal on humble bundle that included a range of books and videos in this area for a mere 18 USD. What a deal that was and I literally threw myself on getting this bundle deal and sure am happy I did.
This helped me find my starting point to the journey. So as I come primarily from a software engineering background, I do have limited knowledge of mathematics and statistics for that matter and most of what I learned, I have mostly forgotten by now(old age catching up?). My first point of entry was to this video course I found on this bundle called Mathematical foundation for AI and machine learning. For a noob like me, this is such an amazing starting point as the author starts off from the basics of linear algebra and goes on to cover topics such as multivariate calculus and probability theory.
Personally, I love to know how something works before using it. The point is(at least for me) not to understand all the nitty gritty details of each formula that is presented, but the concept behind it and why and when you would use it. My rationale here is that there are plenty of libraries out there(especially in Python) that does this for you and knowing how each algorithm operates, works perfectly for me.
Next up, while learning the mathematical details, I wanted to get my hands dirty at the same time so that I can get to use what I learn. Small steps matter. Browsing through the books I purchased via the bundle I purchased, I stumbled upon this book (Do not worry, it is not a referral link, you can click it to see what the book was). I am very pleased with this book so far. It started off easing you to machine learning concepts in general and the approach you would take in tackling such a problem. It then goes on to doing real world examples using Python with anaconda. Yes you read it right, I said anaconda. Not the reptile and nor is it the song from Nick Minaj. It is distributed with Python and R along with most of the libraries you would need in the data science space. It also comes with conda, the package and virtual environment manager which helps when you want to install packages you want to work with. I started off using mini conda for now and with the help of virtual env, created an environment with Python3.
I learn and retain so much when I blog about the things I learn and thought to share things that I learn as I go through this journey. After all, sharing is caring and there is so much I can learn from the wider audience that I hope reads this post.
It would be very much appreciated if you, my awesome readers, share your own thoughts on how you started your journey and anything else I should work on as I go through this journey through data science.
