It takes the average reader 5 hours to read Approaching (Almost) Any Machine Learning Problem by Abhishek Thakur
Assuming a reading speed of 250 words per minute. Learn more
This is not a traditional book. The 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 option. This 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 is more oriented towards how and what should you use to solve machine learning and deep learning problems. The book is not for 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 laptop/workstation where you can code along. Table 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 - Hyperparameter optimization - Approaching image classification & segmentation - Approaching text classification/regression - Approaching ensembling and stacking - Approaching reproducible code & model serving There are no sub-headings. Important terms are written in bold. I will be answering all your queries related to the book and will be making YouTube tutorials to cover what has not been discussed in the book. To ask questions/doubts, visit this link: https://bit.ly/aamlquestions And Subscribe to my youtube channel: https://bit.ly/abhitubesub
Approaching (Almost) Any Machine Learning Problem by Abhishek Thakur is 300 pages long, and a total of 75,000 words.
This makes it 101% the length of the average book. It also has 92% more words than the average book.
The average oral reading speed is 183 words per minute. This means it takes 6 hours and 49 minutes to read Approaching (Almost) Any Machine Learning Problem aloud.
Approaching (Almost) Any Machine Learning Problem is suitable for students ages 12 and up.
Note that there may be other factors that effect this rating besides length that are not factored in on this page. This may include things like complex language or sensitive topics not suitable for students of certain ages.
When deciding what to show young students always use your best judgement and consult a professional.
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