---
product_id: 206456932
title: "Approaching (Almost) Any Machine Learning Problem [Print Replica] Kindle Edition"
brand: "abhishek thakur"
price: "102 kr"
currency: DKK
in_stock: true
reviews_count: 10
url: https://www.desertcart.dk/products/206456932-approaching-almost-any-machine-learning-problem-print-replica-kindle-edition
store_origin: DK
region: Denmark
---

# Approaching (Almost) Any Machine Learning Problem [Print Replica] Kindle Edition

**Brand:** abhishek thakur
**Price:** 102 kr
**Availability:** ✅ In Stock

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- **What is this?** Approaching (Almost) Any Machine Learning Problem [Print Replica] Kindle Edition by abhishek thakur
- **How much does it cost?** 102 kr with free shipping
- **Is it available?** Yes, in stock and ready to ship
- **Where can I buy it?** [www.desertcart.dk](https://www.desertcart.dk/products/206456932-approaching-almost-any-machine-learning-problem-print-replica-kindle-edition)

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## Customer Reviews

### ⭐ 







  
  
    Frenetic & shallow
  

*by W***E on Reviewed in the United States on July 5, 2020*

Well, I kind of expected this brochure to be an overview but I didn't expect it to be that shallow and chaotic.The author is jumping from statement to statement, from t-SNE to cross-validation, Sturge's rule and God knows what else all these by page 27.There are no instructions on how to obtain the datasets (I know how and where but that's not the point, we strive for reproducibility here and we are not expected to 'believe' in certain things like in Santa or Yeti).The phrases 'as you can see on this graph' are nothing but funny. Where did that come from?Perhaps, it is well worth 9$ but no more than that. I will stick to the book by Aurelien G.To sum it all up, I will vote for kernels and medium posts but this...it ain't a book :)I can't teach things but I am not trying to. Perhaps we should stick to stuff we are good at?

### ⭐⭐⭐⭐⭐ 







  
  
    An excellent book on building predictive models.
  

*by S***S on Reviewed in the United States on July 5, 2020*

Kaggle has a strange reputation within the data science community. On one hand it's a great source of innovation in a range of sub-fields and when solving a similar problem to an existing Kaggle competition seeing how it was approached by high ranking teams is very valuable. On the other it is a distorted version of what data science actually is in the real world. Usually the (clean) data is provided to you in Kaggle whereas sourcing, collecting and cleaning data is normally a big chunk of a working data scientists life. Finally the approaches in Kaggle competitions are often all about squeezing that tiny improvement out of large numbers of ensembled models. In the real world concessions towards speed, simplicity and interpretability have to be made.The author Abhishek Thakur was the first to achieve GM level across all 4 categories on Kaggle (competitions, kernels, datasets and discussion) . Even a single GM level is an exceptionally difficult task requiring immense amounts of time and skill. My worry going into this book was who it was aimed at and what its purpose is; is it just about doing well on Kaggle or will people who work in industry learn something valuable? Is it aimed at advanced modellers who are looking to become truly elite or would someone with a more general background gain useful knowledge?I am pleased to state that this is a book which is very valuable for the working data scientist and the keen Kaggler. The real value is how it allows us to see how a highly skilled predictive modeller approaches new problems. The book is made up of 13 chapters;ch 1 - Setting up your working environmentch 2 - Supervised vs Unsupervised Learningch 3 - Cross-validationch 4 - Evaluation Metricsch 5 - Arranging Machine Learning Projectsch 6 - Approaching categorical variablesch 7 - Feature engineeringch 8 - Feature selectionch 9 - Hyperparameter optimisationch 10 - Approaching image classification & segmentationch 11 - Approaching text classification/regressionch 12 - Approaching ensembling and stackingch 13 - Approaching reproducible code & model servingWhile Kaggle is great at discussing a highly placed final entry, the value of this book is a walk through of the steps taken towards a solution; ch 1 on Setting up your working environment, ch 5 Arranging a machine learning project and ch 13 on Reproducible code and model serving I found particularly valuable learning some neat tricks on laying out a project. These are all valuable topics which can get lost when we ask "How did the solution work" as in reality the final answer involved lots of iteration lost in just seeing the final product. I really liked the way the author's projects were laid out using a config file and model_dispatcher file allowing for quick modification of which algorithm to use. I had not come across this before but it's a great idea which speeds up the iteration of the modelling process. The other elements that I think makes this book such a great learning solution for people beginning their data science journey is that it shows mistakes which the author then discusses in depth. Finally we see many examples of simpler models beating more complex ones a lesson that is hard to accept when you are starting out and keen to apply XGBoost or Neural Networks to all the things.This book is not just for beginners however - even as someone who has worked as data scientist in industry for a number of years I learnt a great deal from the chapters on dealing with categorical variables, feature engineering and feature selection. As the author notes there are other sources for these solutions but they are spread out across numerous blog posts and forums - having them in a book makes things much easier. I work in customer analytics so when building predictive models a lot of time in spent on feature engineering and feature selection - I learnt a couple of tricks which will be valuable for new projects at work. The book even includes a section on using embeddings on tabular data - a neat approach not widely used in my experience.Finally the book amazingly includes chapters on computer vision problems using PyTorch for classification and image segmentation and nlp using a range of approaches of increasing complexity from bag of words through word2vec to and LSTM and finally a BERT model. The author rightly skips over the complexities of how a CNN or LSTM and Transformer work, but gives enough of a description to get a sense of what is going on. Again the author emphasises the valuable lesson of starting with simpler models and approaches and only then increasing the complexity with constant comparison to a baseline. The author hints (perhaps jokingly) he is considering work on similar books on Computer Vision and NLP - I hope the success of this book encourages him to seriously consider doing this.It is an amazing achievement that the author has created a book which allows the reader to build strong models in a such broad range of domains. The book is well written with the code in particular being excellent. There were one or two spots where the written phrasing was a little hard to follow but these were rare and overall I enjoyed the writing style. The book is eminently practical so the reader will need to find other sources for the theoretical workings of the algorithms used as they gain more experience. Given the breadth the book achieves this is perfectly acceptable. Finally a small technical issue I had with the Kindle version was the lack of a table of contents accessible via the Kindle menu. Not a big thing but does make navigating the book a little trickier than it needs to be.Overall this is an excellent book full of hard won wisdom from a very talented data scientist and educator. I would happily have paid 4 or 5 times its current price and still been very happy with my purchase. I will be highly recommending this book to friends and colleagues who work (or hope to work) in the field.

### ⭐⭐⭐⭐⭐ 







  
  
    Great for machine learning college students!
  

*by J***U on Reviewed in the United States on September 22, 2020*

I believe that there's a fairly large demographic of people in the community who have probably taken some machine learning course in college. Learning how to program and implement algorithms from scratch is important since this gives us an idea of the inner workings of the theory and mechanics behind the libraries that are implemented in practice. However, I am guessing that it is likely that most of the datasets previously used are quite plain and probably not that difficult or interesting (e.g., the iris dataset). In other words, our experience in academia leaves it quite unprepared regarding how to take the next step.I think that this book does a great job of guiding those who are likely trapped in the academic experience of machine learning, where our work is centered around class assignments or projects that are too cookie-cutter and poorly organized. This book provides some guidance by answering many left-over questions regarding how we can understand a system/methodology that will allow us students to go from homework assignments and towards approaching real-world problems in a professional manner.

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*Last updated: 2026-03-07*