MBA wala in Tech

  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...

As I resolved to delve deeper into Deep Learning, I came across the book  Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville—a must-read recommended by many ML practitioners and academics. However, as I started reading it, I quickly realized a good grasp of linear algebra is essential. This realization led me to search for resources to catch up on linear algebra concepts. After considerable searching, a colleague recommended a series of lectures by Prof. Gilbert Strang. I got hold of the lecture videos, and boy, was I delighted! This series of lectures is meant for people with a rudimentary understanding of matrix algebra who want to refine their understanding of how linear algebra serves as the foundation for Deep Learning as well as various other important fields: Computer Graphics:   Understanding transformations and projections Data Science:   Utilizing techniques like SVD for dimensionality reduction Engineering:   Solving systems of equations ...

  When I decided to dive into Machine Learning, my first challenge was mastering Python, the language that powers it all. With so many resources available, from MOOCs to books, I had to choose where to start. I've always believed that books are the best learning tools because they tend to be exhaustive. Pick a good one, and you’ll learn almost everything you need to know—enough to get you going! Initially, I thought MOOCs diluted their content to fit into a short format, but I've started to change my mind. More on that later! So, I began by making a list of books that suited my learning needs and included them in my learning plan. These books helped me get up to speed on my Python skills and learn Pandas and Numpy, the two most popular packages used extensively in the field of ML. They are: Head First Python by Paul Barry Python Crash Course by Eric Matthes Learning Python by Mark Lutz Python for Data Analysis by Wes McKinney Fluent Python by Luciano Ramalho I’m still read...

Hi, I’m Debarchan Mitra. I’ve always believed in pursuing a career that excites you—it's the best way to avoid those dreaded Monday blues. This belief led me to study finance at one of India’s prestigious B-Schools. I figured I’d build a career out of it, even if I wasn’t sure how. But no matter how hard I tried, I couldn't picture myself hustling in Mumbai, working at a top asset management firm or investment bank, managing other people's money. I still loved coding, though. In 2014, I found myself working in an IT firm in Bangalore as a Business Analyst. Like any B-School graduate, I started making career plans, but they didn't sound any different from my batchmates' and friends' plans. Around this time, Machine Learning was making waves and had become the talk of corporate India. Out of sheer curiosity, I decided to start learning about it—not to make a career out of it since I already had other plans. But as I dipped my toes in, I got more and more drawn to ...