---
product_id: 249677392
title: "Approaching (Almost) Any Machine Learning Problem"
brand: "abhishek thakur"
price: "B/.87"
currency: PAB
in_stock: true
reviews_count: 6
url: https://www.desertcart.com.pa/products/249677392-approaching-almost-any-machine-learning-problem
store_origin: PA
region: Panama
---

# Approaching (Almost) Any Machine Learning Problem

**Brand:** abhishek thakur
**Price:** B/.87
**Availability:** ✅ In Stock

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- **What is this?** Approaching (Almost) Any Machine Learning Problem by abhishek thakur
- **How much does it cost?** B/.87 with free shipping
- **Is it available?** Yes, in stock and ready to ship
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## Description

Approaching (Almost) Any Machine Learning Problem

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## Customer Reviews

### ⭐⭐⭐⭐⭐ 5.0 out of 5 stars







  
  
    Perfect for me
  

*by M***B on Reviewed in the United Arab Emirates on 1 October 2021*

The kind of book I was looking for. Not much mathematics not much beating around the bush. Straight code to show you how it's done. That's it. The real deal.I wanted to give it 4.5 stars if that was possible due few minor issues highlighted by some of reviewers. But those are minor points. No book is perfect for everyone. Probably the author can make some minor tweaks in the next edition. But for me, this perfect. So much to learn from it. Amazing value.

### ⭐⭐⭐⭐ 







  
  
    Well-thought out applied machine learning book
  

*by G***R on Reviewed in Germany on 19 August 2021*

This is a very solid machine learning book with a strong focus on implementing things in Python/sklearn/PyTorch and how to do things in practice. I bought the book on the strength of the author's Kaggle success (well-known Kaggle Grandmaster) and excellent YouTube videos. I was not disappointed and got what I expected. I debated endlessly with myself whether this is four or five stars, because I would have liked a bit more depth/rationale for approaches (of course, that's a trade-off vs. the book length - and yes, I realized the cover text did tell me, but also things like pro-and-con of some methods might have been expanded), I had hoped even more types of problems/recent ideas would be discussed (don't get me wrong, a wide range of critical topics is covered really well - we are more talking about whether EfficientNet should be discussed in addition to ResNet, or whether more should be said on alternative loss functions like focal-loss, or the latest improvements over U-Net etc.), I wondered whether some topics were glossed over a little/downsides of some things not really discussed, and some minor formalistic complaints (no index, book title on the page headers instead of chapter titles, personally I might have liked the table of contents to show things below the chapter). Perhaps the length of some bits vs. other bits felt a tiny bit uneven (I guess this is just not an academic text-book that got polished over and over - which also makes it an accessible and a very pleasant fun read).But enough on the minor quibbles, here's some things that I really liked:1. Covering cross-validation really early on before starting with any supervised models: Definitely the right choice, excellent decision! In industry, too many people are sloppy with their model evaluation, emphasizing it this much is something people should be exposed to in more books. In fact, overall there's some pretty good didactic choices in how the material is arranged.2. I had not been sure about the approach of putting so much code directly into the book, but it works (it helps that the code is very readable/well-explained).3. Without trying to directly be a book primarily about PyTorch, it's actually a pretty good bare-bones starter pack for PyTorch (even if I prefer the more in-depth book by the fast.ai team).4. I like the author's writing style. Don't expect formalistic statistics or computer science text book language (and most definitely don't expect theorem - although some papers are referred to, but mostly when the author feels it would be worth your time to read them, there is no attempt to provide a reference for every statement or give a reference to who invented what method when). To get a feel, watch some of the authors YouTube videos - to me the book is a more concise, more polished product with a little bit more explanation of methods/why we use them (as said, maybe a little less than I prefer, but that's taste).4. Production value: The book is independently published, but with the exception of some of the complaints I mentioned above, I was very happy with design/layout/paper quality etc.

### ⭐⭐⭐⭐⭐ 







  
  
    Nice code explanations to get into ML
  

*by P***5 on Reviewed in Germany on 11 March 2023*

Nice code explanations to get into ML

## Frequently Bought Together

- Approaching (Almost) Any Machine Learning Problem
- Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications (Grayscale Indian Edition)
- Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps (Grayscale Indian Edition)

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*Store origin: PA*
*Last updated: 2026-02-20*