epub ↠ Approaching ê Almost Any Machine Learning Problem ï abhishek thakur

ebook Approaching

epub ↠ Approaching ê Almost Any Machine Learning Problem ï abhishek thakur ï ❰Reading❯ ➶ Approaching (Almost) Any Machine Learning Problem Author Abhishek Thakur – Goproled.co.uk This is not a traditional bookThe book has a lot of code If you don't like R optimization Approaching image classification segmentation Approaching text classificationregression Approaching ensembling and stacking Approaching reproducible code model serving There are no sub headings Important terms are written in boldI will be answering all your ueries related to the book and will be making YouTube tutorials to cover what has not been discussed in the book To ask uestionsdoubts please create an issue on github repo Subscribe to my youtube chann It's been 11 days since I got this book I have refreshed so many topics with code and learned many new I wish to lay out a few points which helped me and I can carry from the book but before that thank you Abhishek Thakur for placing things in order And trust me I am waiting for the book you mentioned on page 271 Page 3 If you didn't code you didn't learn I think this is the best way to learn I went through a theoretical module on machine learning and initially I wasn't getting much and the moment I started coding for an assignment of the same module every bit of the module started making sense that's so common right? This book has one thing particular that is code with concise explanations I love the way the author has derived us throughout different chapters It helped me to refresh certain topics in order and adapt to the new ones uickly Since I was familiar with basics I jumped to page 185 and finished till 271 I know that's the wrong way but I don't know why I did that p later this book forced me to go through the starting chapters as well since it is well written and organized I would suggest anyone with basic knowledge of machine learning concepts must go through this book and start addressing practical problems

Abhishek Thakur Ø Almost Any Machine Learning Problem book

This is not a traditional bookThe book has a lot of code If you don't like the code first approach do not buy this book Making code available on Github is not an optionThis book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning The book doesn't explain the algorithms but isoriented towards how and what should you use to solve machine learning and deep learning problems The book is not fo 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 codesAnyways 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

text ´ Almost Any Machine Learning Problem Ø Abhishek Thakur

Approaching Almost Any Machine Learning ProblemR you if you are looking for pure basics The book is for you if you are looking for guidance on approaching machine learning problems The book is best enjoyed with a cup of coffee and a laptopworkstation where you can code alongTable of contents Setting up your working environment Supervised vs unsupervised learning Cross validation Evaluation metrics Arranging machine learning projects Approaching categorical variables Feature engineering Feature selection Hyperparamete 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 amazingI really liked the flow of the book from basic concepts to covering images and text problems advanced concepts like Entity embeddings and what notThe codes are written perfectly and in such a way that it develops a great habit of writing good uality code for any data scientist or enthusiast The author has made sure that the code is reusable and can be taken into productionPS Don’t think too much just go ahead and buy it It’s Amazing