ApproachingAny Machine Learning Problem
by Abhishek Thakur
Should I read this?
Recommended by 1 source and appears in Machine Learning.
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 a...
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Why recommended
Recommended by 1 source and appears in Machine Learning.
Recommended by notable people
People and public figures who have recommended this book.
Recommendation Signals
Recommendation proof is sourced from public posts, interviews, reading lists, and cited references.
Kirk Borne
“[Excellent Book] Approaching (Almost) Any #MachineLearning Problem: by @abhi1thakur (4X Kaggle Grandmaster) + See article: ——————— #BigData #AI #DataScience #DataScientists #DeepLearning #BeDataBrilliant #FeatureEngineering #Python”
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Appears In
Not sure if this is the right fit?
Consider Life 3.0 by Max Tegmark. Recommended by 18 sources.
“Life 3.0 reads like a long, wide-ranging conversation with a physicist who loves big if-then thought experiments. The useful part is its panoramic sweep across possible AI futures—from job automation to cosmic colonization—forcing you to consider timelines you might otherwise avoid. The limitation is that the speculative breadth often outruns the depth; chapters can feel meandering, and some readers will find the cosmic-scale scenarios too detached from practical concerns, making it hard to ground in real urgency.”
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Each recommendation is collected from a public source — interviews, articles, or curated lists — and linked to its original URL. Books with many verifiable recommendations from respected people rank higher.