I couldn't quite get through this book, even after a year and a half of trying. While I only made it halfway, I'm keeping it around as a handy reference for future projects that might require similar tools. It's a shame I couldn't finish it, but its utility as a resource is still valuable.
This book is truly amazing, offering a fantastic collection of lectures, code examples, and insightful perspectives on Machine Learning. It's a standout resource for anyone delving into the field.
This book is a fantastic starting point for anyone looking to get into machine learning, especially if you're at an intermediate level. It's incredibly thorough and would be a great resource for both graduate and undergraduate students diving into the subject.
This is hands down the best introductory book for Python and machine learning I've encountered. It genuinely focuses on teaching the concepts, which is a refreshing change. I'd also recommend checking out the author's explanation on Goodreads about their motivation for writing it; it really clarifies their approach.
While the book is packed with examples for every concept, the Polish translation is truly abysmal. It's a significant drawback that makes me question the quality of the original English version. I sincerely hope the English edition doesn't suffer from the same translation issues.
This book offers a solid foundation for anyone diving into Machine Learning research. It covers the essential concepts clearly and concisely, making it a valuable resource for building a strong understanding. I'd recommend it to anyone looking to establish their baseline knowledge in the field.
This book was excellent! I really enjoyed it and would highly recommend it to anyone looking for a great read.
This book was a real brain-bender, but ultimately, it paid off handsomely. Tackling the concepts within was tough, but the sense of accomplishment after mastering them for the Microsoft Imagine Cup Competition was immense. It's definitely a resource that pushes you to grow.
This book offers a solid overview of Machine Learning, perfect for beginners looking to grasp the general concepts. While it provides a great starting point, readers will need to seek out additional resources to delve deeper into specific topics. A concluding chapter with integrated exercises would have been a valuable addition to reinforce the material covered.
This book offers a fantastic blend of mathematical theory, intuitive explanations, practical Python code, and real-world model applications. For someone with my background, it presents a remarkably logical and accessible path into the world of machine learning.
This book is a fantastic introduction to machine learning, striking a great balance between clear mathematical explanations and really useful code examples. I especially appreciated how it consistently pointed me toward resources for further study. It's a solid, practical guide that I'd recommend to anyone looking to get started in the field.
This book is a fantastic resource for anyone diving into machine learning, especially if you're concerned about the math involved. The explanations for common ML algorithms and clustering techniques are incredibly clear and accessible. Plus, the well-documented GitHub repository is a huge bonus for practical application. It's definitely a solid reference point for learners.
This is a truly fantastic introduction to machine learning, striking an excellent balance between mathematical theory and practical implementation. It clearly explains the underlying math, how it translates to Python code, and then guides you through using libraries like scikit-learn and TensorFlow. While it assumes some Python and math familiarity, the explanations are otherwise clear and elegant, with particularly insightful sections on model evaluation and deep learning concepts. It's an incredible resource, only slightly held back by a lack of practice problems.
This book is pretty good, though I'd probably rate it a 4.75 out of 5. It strikes a decent balance between the math and Python concepts, even if that balance felt a little off at times. Still, it's definitely one of the stronger entries I've encountered on this topic.
This book was a significant time investment, but ultimately, it was absolutely worth every minute. It served as an excellent guide for my initial foray into ML/DL, progressing logically from fundamental concepts to advanced topics like Reinforcement Learning, and thoroughly explaining crucial areas such as boosting, neural networks, CNNs, RNNs, and GANs. What's particularly great is that it doesn't overwhelm you with complex math, providing just the right amount to get you up and running effectively.
This book is an absolute gem for anyone, especially scientists, looking to dive into machine learning. It strikes an incredible balance between being technically sound and easy to understand, providing a solid foundation without dumbing things down. You'll even build your own neural network early on, which is a fantastic way to learn. I consistently recommend this accessible yet thorough introduction to my colleagues.
This book offers a fantastic starting point for getting hands-on with Python's machine learning libraries like scikit-learn, SciPy, TensorFlow, and Keras, and the included code examples are quite helpful. However, if you're looking to truly grasp the mathematical underpinnings of these techniques, you'll likely need to seek out other resources, as that aspect is rather underdeveloped here. The author does provide a wealth of references for further exploration, which is a definite plus for those wanting to dive deeper. Plus, their responsiveness on the GitHub repository is commendable.
This book offers a solid introduction to machine learning, skillfully blending theoretical underpinnings with practical Python code examples. While it doesn't delve into the deepest theoretical waters, which is perhaps for the best given the subject's complexity, it remains a technically rich resource. The accompanying GitHub repository is a fantastic asset, allowing for interactive exploration of the code samples, though a few snippets might need a bit of tweaking to run smoothly. Ultimately, it's an excellent way to get acquainted with various ML approaches, but be prepared to dive deeper for true mastery.
As a beginner diving into machine learning with Python, this book was a fantastic starting point. It walks you through everything from logistic regression to neural networks, complete with code examples you can run yourself. While some of the material might feel a tad dated, the author's explanations are clear and accessible for anyone with a grasp of probability and linear algebra, making it a solid foundation for aspiring ML practitioners.
This book is a solid resource for getting acquainted with the scikit-learn library. However, the sections covering Theano and embedding code into web applications felt a bit extraneous to the core topic. For Theano, I'd personally lean towards scikit-flow, as it offers a more familiar interface for scikit-learn users.
This book offers a fantastic starting point for anyone looking to dive into machine learning with Python. The inclusion of readily available code examples on GitHub is a huge plus, making it incredibly easy to follow along and experiment.
This textbook strikes a fantastic balance, offering more mathematical depth than many introductory Python machine learning texts, which was perfect for my background. I particularly appreciated the occasional coding-from-scratch approach to models; while potentially overwhelming for absolute beginners, it really sharpens computational thinking. The inclusion of links to academic papers and lecture slides is a huge plus for clarifying theoretical concepts. While some algorithm descriptions could be clearer and more emphasis on model assumptions would be beneficial, this book is an excellent resource for those with a quantitative background looking for a solid introduction to key models and libraries like scikit-learn.
This book is an outstanding practical guide to machine learning, expertly using Python and its extensive open-source libraries. It thoughtfully assumes no prior ML knowledge, though a Python, linear algebra, and calculus background is beneficial. The author covers everything from fundamental ML concepts and algorithms, including their mathematical underpinnings, to crucial practical aspects like data preparation, visualization, and evaluation. You'll find plenty of hands-on advice, links to real-world datasets, and downloadable code, making it both an engaging read and a valuable reference.
This book is a brilliant introduction to machine learning using Python. It strikes a great balance, offering solid theoretical background for each concept before diving into practical Python implementations. I particularly appreciated how it builds Neural Networks from the ground up, which really helps in grasping core concepts like back-propagation. Plus, the coding style presented is both efficient and professional, a valuable skill for any aspiring data scientist. It's a strong recommendation for anyone with an intermediate grasp of machine learning and Python.
This book is fantastic! It dives deep into the mathematical underpinnings of popular machine learning algorithms and guides you through their implementation. What really sets it apart, though, are the chapters dedicated to crucial topics like data cleaning and handling missing data, which are often overlooked. It's a perfect complement to resources like Andrew Ng's ML course, filling in those essential practical gaps.
While the information presented was solid, I'm not sure I would have picked this up on my own. The author's attempts at sounding 'cool' occasionally felt a bit forced and detracted from the experience. However, I did appreciate the structured layout, particularly the way theory was followed by examples and key terms were helpfully bolded.
This book offers a solid, practical tour of essential ML techniques using scikit-learn. While it's not the most cutting-edge library for advanced methods like gradient boosting or deep learning, it excels for SVM and regression, and the explanations and examples provided are truly helpful.
This book dives deep into Machine Learning, and I found the sections on algorithm training and clustering particularly insightful. While some chapters, like embedding ML into web apps and Theano training, felt a little tangential to the main focus, the core content was definitely valuable.
This book is a fantastic resource! The explanations are wonderfully concise, and it's great that all the necessary math is included, along with helpful example code. I've already downloaded a PDF copy because I know I'll be returning to this material frequently.
This book is a fantastic introduction to Machine Learning. However, the Spanish translation by Marcombo is unfortunately riddled with errors that will hopefully be corrected in future editions.
