Approaching (Almost) Any Machine Learning Problem Paperback – 30 June 2020

This book is exactly what it claims . It is approach to almost any kind of machine learning problem . The book has a very practical approach towards problem solving and has tones of code . The book is intended to people who knows a bit of background and struggles to start problem solving . The book was and is very helpful for me in day to day life and hence the 5 stars .I want to be clear in the review so that people dont get disappointed after buying the book or refrain from buying the book seeing few negative reviews .The book is immensely helpful for senior professionals like me who are from other branches of software engineering /domain knowledge and still wants to reduce the entry level barrier to get into machine learning and deep learningThe book is immensely helpful for students from programming/non-programming background looking to learn and solve AI/ML problems and are scared to venture into it .The book is immensely helpful for people with a lot of theoretical knowledge but less programming knowledge to apply the same into some cool project .The book is not helpful for people who has a masters in mathematics and looking for reference in writing phd thesis in Machine Learning methods .

Very useful for current era. Thanks

Excellent book. If you have a basic understanding of theoretical concepts of ml and want to improve your practical knowledge , this is the perfect choice for you. Very nicely written code with indents!!!.

I have been following Abhishek's work in Kaggle for few years and he is one among the brilliant problem solvers in data science space.He always tries to share his knowledge with others and this is evident from his kaggle kernels and youtube videos.This book is another addition to this and stays true to its description - deals with applied machine learning starting from regression to docker containers.All the concepts are practical in nature and I am using it as a reference material for my day to day job and tackling kaggle competitions.The ML pipeline used by him is reproducible and flexible for any ML tasks.I would recommend this book to those who are considering a career in ML and want to learn best practices in organizing your projects and code.In one of the youtube videos,Abhishek mentions that contributing a small amount to the courses taught by experts like Andrew Ng is a small way of showing our respect towards them .I did the same by buying this book for Abhishek.Thank you Abhishek and I look forward to your upcoming books as well.Keep sharing your knowledge to the world.

I’ve known Abhishek Thakur through his YouTube channel on ML and frankly I wasn’t surprised with the level of clarity this book has to offer. It demystifies ML problems at a molecular level which is immensely helpful for practitioners or even for folks trying out at Kaggle competitions. The codes are very fundamental and easy to understand, the pipeline is replicable and fungible as well. For those who’re looking to excel at kaggle competitions or better yet exploring a career in ML - this would serve as a strong foundational platform. Also at this price point - I really don’t see any other book in the market with such level of conceptual clarity and usability. The book pretty much stands true to its title - you can approach almost any ML problem with it.

I am a Data Scientist/ AI Engineer working for the last 3 years in the industry. This is my honest review -As the book name suggests this is the best book that you could get on practical machine learning and data science. The author has written the book with great dedication and the explanations at each section is amazing.I really liked the flow of the book, from basic concepts to covering images and text problems, advanced concepts like Entity embeddings and what not!The codes are written perfectly and in such a way that it develops a great habit of writing good quality code for any data scientist or enthusiast. The author has made sure that the code is reusable and can be taken into production.P.S. - Don’t think too much, just go ahead and buy it!!! It’s Amazing!

First of all, i bought the Kindle version and in what price it is being shared to the community is damn unbelievable. A note to future customers who will really appreciate the beauty of this Book, get your basics right and then jump on to this Book because it gives you an idea on how to approach the problems and how to code which is the most important thing for Data Scientist, traditionally we google and copy paste, but in this book you'll get on with the coding part, understand what exactly you're doing and implement the stuff. Well i can go on and on but you'll seriously appreciate it and get the knowledge that this book shares only by experiencing it. So go on and buy it already man !! :D ( PS : Abhishek Thakur, you rock )

It is just a collection of notes and codes that we keep in some random text file, just to resuse it in future. It doesn't give you in depth knowledge of whats and hows of things written in it. Or just maybe I expected something else from this and it turned out to be a simple book of codes.Anyways, it would have been easier just to create a github repo with proper readme files and all, instead of writing a whole book for it.

It is an awesome ML book. If you read few ML books and you are not sure ML workflow, what ML model to choose, how to do it? Then this book is the one you need. It can help you put everything you learned ML together. So it is not a beginner book. Thanks.

Many useful tips, i never figured out by my self, worth every cent

This is one of the best hands-on ML books that I have read so far. The way that the author explains make it easy to understand. Also, I liked a lot the coding style and the ML arrangement shown in the book.

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.

Just finished reading the book "Approaching (Almost) Any Machine Learning Project" recently published by Abhishek Thakur .I must admit that i was positively surprised by the atypical content/approach Abhishek implemented in his book.Some of the things that i found really nice:- he motivates and stresses best practices for building and evaluating machine learning models for the most common problems,- he shares different tips & tricks you'll generally spend a lot of time learning while implementing concrete projects / kaggle challenges,- he advocates for a greedy approach towards solving different machine learning problems (always set a clear objective/target and start simple); Abhishek demonstrated that all along the book while discussing different problems.You'll not find in depth explanation of ML algorithms in this book but instead, very good advices on how to properly leverage and apply them in order to solve concrete problems.Thanks a lot Abhishek for the amazing work!