Data Science From Scratch: First Principles with Python, Second Edition (Greyscale Indian Edition)

I am almost through this book. I regret not starting with this book for my ML journey. This book teaches you what's going on behind the scene. It becomes very important to have this understanding. So, this is a MUST read !!.

Good

There were no damages and the papers were thick Grey Scale edition Number of pages: 384

It's an excellent book for new learners

good book

The book is being sold exactly at the same price as MRP. Should provide at least some discount after considering that even all graphs and plots are printed in black and white as well.

Very good book for Data Science. The author discussed every aspect in detail. Easy to understand by graduates, researchers and market professionals.

You can buy ,just for revision purchase.

Did you see something on the news about ChatGPT, Stable Diffusion, or some other big development that made you want to look into machine learning? Maybe you truly plan on entering data science as a field but don't know where to start? Or perhaps you've seen one of the author's brilliant/hilarious talks about why he doesn't like Jupyter Notebooks or how to answer the infamous "FizzBuzz" programming interview question using Tensorflow neural networks (seriously, look up Joel Grus on YouTube). If you know a little bit of Python, a little bit of relevant math, and want to go into any data science or machine learning path, then this book is a must-have. It certainly won't be the only resource you'll need, but it helps you get the most out of other content you'll likely look into later (like how to code up a machine learning pipeline, or maybe a large language model if you're really adventurous). Far too many machine learning lessons out there just tell you to import certain Python libraries (scikit-learn for example) and start using them without giving you any basic understanding of how those imported functions even work to begin with. Even to this day there are still college courses and coding bootcamps that ask you to download a Jupyter Notebook file and just hit "Shift + Enter" and look at the output. You're not going to learn how to code that way!!! Joel Grus does an excellent job of filling in this gap by teaching you more Python than what a statistics professional would usually know and more math than what a typical software developer would know. And that's key if you want to go into a field that relies on both. All the information for Python and math that you need to get started is here. It's 27 chapters that get you familiar with Python and how to use it, as well as the math used in data science and ML (linear algebra, probability and statistics, algorithms, etc). You eventually learn enough of both as you go through the chapters to start applying what you learn for some real-world usage. I've had this book for years and it's still as useful as when it first came out, but the only exception I've seen is that the Twitter API tutorial in the book no longer applies to the paid format that Twitter now uses to access that feature. The tutorial is still good for learning how API's get put to use. Once you've read this book and have gotten familiar with all it has to offer, your next step will probably involve looking into a book about how to actually use pre-built data science libraries (like what you find in the Anaconda distribution of Python). This book may turn out to be heavily responsible for my first startup, but that's a story for later.

Buen producto llego bien

A must-read in this era.

Joel's method of explaining is both entertaining and very useful

The book is useful to grasp the basic concept behind data science. However it gets pretty messy as the topics become more complex, especially when the python code is shown without too much of explanations. If you need a book to learn python for data science, there are many other alternatives.