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5 books on Machine Learning [PDF]
November 04, 2024 | 22 |
Books on Machine Learning are covering supervised and unsupervised learning, deep learning, and reinforcement learning. They describe the process of algorithm design, feature engineering and model evaluation. Moreover, these books often include practical examples and best practices, enabling startups to build and fine-tune their machine learning models for real-world applications.
1. Machine Learning For Dummies
2021 by John Paul Mueller, Luca Massaron
Imagine a book that gently picks you up from your comfortable couch of ignorance and sets you down, blinking, in the bright land of machine learning. "Machine Learning For Dummies" does exactly that—without assuming you’ve even seen Python before (no, not the one with fangs, the one with syntax). You’ll be whisked through the history of AI, invited to play with Python and TensorFlow and assured that the math is friendlier than you’d think. In true "for dummies" fashion, this guide aims to turn you from a curious newbie into a confident explorer of the machine learning landscape, faster than you can say “artificial intelligence.”
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2. Interpretable Machine Learning
2020 by Christoph Molnar
If ever you felt that machine learning was a mystical black box full of wizardry and secrets, Christoph Molnar is here to hand you a flashlight. "Interpretable Machine Learning" is a quest to make sense of the often opaque decisions of machine learning models—whether they’re playing doctor or making financial predictions. Starting from the simple (think decision trees) and moving to the mind-bendingly intricate (hello, Shapley values), this book reveals how models come to their conclusions. It's the magic decoder ring for anyone who wants machine learning to speak in plain language rather than techno-babble.
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3. Machine Learning Algorithms
2017 by Giuseppe Bonaccorso
Giuseppe Bonaccorso’s "Machine Learning Algorithms" is a toolkit for aspiring data alchemists—those who dream of turning numbers into predictions, insights, and, occasionally, a touch of magic. He assumes you've tinkered with Python or R and are ready to dive deeper into the world of algorithms, whether it's clustering your data like an obsessive tidier or teaching a computer to recommend movies. From linear regression to support vector machines, this book is a mix of hands-on recipes and conceptual deep dives, ensuring you'll never look at a dataset quite the same way again.
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4. Understanding Machine Learning: From Theory to Algorithms
2014 by Shai Shalev-Shwartz, Shai Ben-David
"Understanding Machine Learning" is a bit like being dropped into a machine learning boot camp—except there are no burpees, just lots of mathematical proofs. Shai and Shai (no, it’s not a machine learning model name, just two authors) break down the intricacies of learning theory and algorithms with the same meticulous care as a watchmaker assembling tiny gears. If you've ever thought that machine learning might have more to it than “throwing data at neural networks,” this book’s deep exploration of convexity, stability and structured output learning will give you the intellectual tools to appreciate the field from the inside out.
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5. Machine Learning: The Art and Science of Algorithms that Make Sense of Data
2012 by Peter Flach
Peter Flach’s book is like a tour guide taking you on a whirlwind adventure across the vast expanse of machine learning. Starting with a simple spam filter and ending in the maze of ROC analysis, Flach ensures you’re never lost for long—each topic is connected to something tangible, something you can chew on. He manages to pack in logical, geometric and statistical perspectives without ever losing that twinkle of “let me show you something cool.” From everyday spam filtering to esoteric matrix factorizations, this is the book for those who like their science mixed with a bit of artistic flair.
Download PDF
How to download PDF:
1. Install Google Books Downloader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
1. Machine Learning For Dummies
2021 by John Paul Mueller, Luca Massaron
Imagine a book that gently picks you up from your comfortable couch of ignorance and sets you down, blinking, in the bright land of machine learning. "Machine Learning For Dummies" does exactly that—without assuming you’ve even seen Python before (no, not the one with fangs, the one with syntax). You’ll be whisked through the history of AI, invited to play with Python and TensorFlow and assured that the math is friendlier than you’d think. In true "for dummies" fashion, this guide aims to turn you from a curious newbie into a confident explorer of the machine learning landscape, faster than you can say “artificial intelligence.”
Download PDF
2. Interpretable Machine Learning
2020 by Christoph Molnar
If ever you felt that machine learning was a mystical black box full of wizardry and secrets, Christoph Molnar is here to hand you a flashlight. "Interpretable Machine Learning" is a quest to make sense of the often opaque decisions of machine learning models—whether they’re playing doctor or making financial predictions. Starting from the simple (think decision trees) and moving to the mind-bendingly intricate (hello, Shapley values), this book reveals how models come to their conclusions. It's the magic decoder ring for anyone who wants machine learning to speak in plain language rather than techno-babble.
Download PDF
3. Machine Learning Algorithms
2017 by Giuseppe Bonaccorso
Giuseppe Bonaccorso’s "Machine Learning Algorithms" is a toolkit for aspiring data alchemists—those who dream of turning numbers into predictions, insights, and, occasionally, a touch of magic. He assumes you've tinkered with Python or R and are ready to dive deeper into the world of algorithms, whether it's clustering your data like an obsessive tidier or teaching a computer to recommend movies. From linear regression to support vector machines, this book is a mix of hands-on recipes and conceptual deep dives, ensuring you'll never look at a dataset quite the same way again.
Download PDF
4. Understanding Machine Learning: From Theory to Algorithms
2014 by Shai Shalev-Shwartz, Shai Ben-David
"Understanding Machine Learning" is a bit like being dropped into a machine learning boot camp—except there are no burpees, just lots of mathematical proofs. Shai and Shai (no, it’s not a machine learning model name, just two authors) break down the intricacies of learning theory and algorithms with the same meticulous care as a watchmaker assembling tiny gears. If you've ever thought that machine learning might have more to it than “throwing data at neural networks,” this book’s deep exploration of convexity, stability and structured output learning will give you the intellectual tools to appreciate the field from the inside out.
Download PDF
5. Machine Learning: The Art and Science of Algorithms that Make Sense of Data
2012 by Peter Flach
Peter Flach’s book is like a tour guide taking you on a whirlwind adventure across the vast expanse of machine learning. Starting with a simple spam filter and ending in the maze of ROC analysis, Flach ensures you’re never lost for long—each topic is connected to something tangible, something you can chew on. He manages to pack in logical, geometric and statistical perspectives without ever losing that twinkle of “let me show you something cool.” From everyday spam filtering to esoteric matrix factorizations, this is the book for those who like their science mixed with a bit of artistic flair.
Download PDF
How to download PDF:
1. Install Google Books Downloader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
