MBA wala in Tech

  So, today I found myself dragging my feet, thinking about which book or course I should write about next. After all, it's tough for any book to follow Introduction to Statistical Learning or ISLR (sorry, I am just big fan of ISLR!). However, in my learning journey, I came across a book that, in my opinion, is a true successor to ISLR: Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow by Aurélien Géron. This book is highly recommended in the ML community, and I've relied on its teachings many times, both in my current and previous jobs. With this gem of a book and many others,  O'Reilly publications is really proving to be at the fore-front of AI-ML education through its books. I have tried to cover the 3rd edition of this book(contrary to the cover image!). So, let's dive in and see what this book has to offer. Who is this book for? Like most of the books and resources I've written about, this one is aimed at people who are just starting out in ...

  Alright, this post is a bit of a side trip on our learning journey. I’ve always believed that having a strong grip on statistics is super important when diving into machine learning. A lot of popular ML algorithms, like linear regression, are basically borrowed from stats. If you’re more into analytics than hardcore ML, knowing your statistics becomes even more critical. The tools and concepts you pick up in stats are insanely useful across all sorts of fields—whether it’s physics, engineering, social sciences, medical research, or even testing new drugs. Whatever buzzwords you’re into—Data Science, AI, Machine Learning, Deep Learning, Analytics, Data-Driven Decisions, Data Modeling—you name it, stats is at the heart of it all. Trust me, getting comfy with statistics will make everything else click into place. But how so? Well, there are two ways to look at data science: Computational View : Here, data is seen as a huge sequence of numbers that need to be crunched by fast algorit...

  Finally, we come to the book that can easily be considered anyone's gateway to Machine Learning and AI: An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani, and Jonathan Taylor. This is my go-to recommendation for anyone asking where to start learning about ML. The book has a huge fanbase in the ML-AI community (myself included), and I have been eagerly waiting to write a blog post about it. “All physical theories, their mathematical expressions apart, ought to lend themselves to so simple a description that even a child could understand  them." -   Albert Einstein  (c.1930), comment to Louise de Borglie Sure, you can find countless books and introductory online courses to start your learning journey. However, they rarely make this field seem approachable, often leaving students feeling that ML is beyond their grasp. An Introduction to Statistical Learning (ISLR) does just the opposite. It introduces the fie...