I've picked this up a second time and while the 'from scratch' approach is appealing for building understanding, it demands working through nearly every code example to keep up. It begins quite simply, but I'm finding it's consuming more time than I feel is justified, and I'm contemplating abandoning it. It might be a fantastic resource for absolute beginners to Python, though.
This book is fantastic for diving deep into the implementation details of various machine learning methods, and it's a great way to solidify your Python basics. The author's commitment to building every function from scratch was something I truly appreciated. While I thoroughly enjoyed the experience, I wouldn't suggest it as a primary resource for learning ML or jumping straight into developing applications. My rating is a 4, mainly because some of the examples lack the necessary data for testing.
This book served as an excellent primer for my MSc in Data Science, starting with fundamental concepts and progressing through general machine learning techniques before touching on advanced topics like MapReduce. While it's not a deep dive, its aim is clearly to provide a comprehensive overview of the field, which it achieves wonderfully.
This book attempts to blend theory and practical code, and it achieves this to a certain extent. However, many of the concepts felt underdeveloped, and the provided code was frequently disorganized and difficult to follow. I often found myself skipping the code itself and focusing only on the comments and explanatory text. The author's inclusion of tests was inconsistent, leaving it unclear what warranted testing and what didn't; these tests often seemed tangential and only added to the overall confusion.
This book left me feeling conflicted. While I appreciated the deep dive into implementing ML and data science algorithms from scratch using only Python's base libraries, which definitely fosters a better understanding, the theoretical explanations felt incredibly rushed. The math could have been presented more clearly with separate formulas, and the absence of any visual aids made grasping complex concepts a real challenge. It's a worthwhile read if you're looking for that foundational knowledge, but temper your expectations; it's not going to blow you away.
This book truly gets to the heart of data science by emphasizing hands-on practice over mere theory. It's fantastic how it starts from scratch with algorithms, meticulously detailing the technical construction behind concepts like regressions, neural networks, and decision trees without relying on pre-built libraries. If you're looking to build a robust understanding of practical Data Science and Machine Learning, this is definitely a recommended read.
After putting off reading this for ages, I finally dove in and found it to be a truly unique and valuable resource. It's definitely not for beginners, though; you'll want a solid foundation in Python, ML, and data science before you start. If you've got that background, this book is fantastic for getting a practical grasp of the core concepts. I did find myself needing to look up external resources frequently, but combining that with the included code really cemented my understanding of ML in a way that theoretical explanations alone couldn't achieve. Highly recommended for those driven to understand the 'how' behind the magic!
This book is exceptionally well-written, striking a great balance between depth and accessibility. While the initial sections are fantastic, the transition into natural language processing felt abrupt and escalated far too quickly. It's not the ideal resource for aspiring data scientists looking for practical application, nor is it comprehensive for those seeking a deep dive into the fundamentals, which are only touched upon very lightly.
While the book's reliance on Python 2 might seem a bit dated given Python 3's widespread adoption, it's not a dealbreaker for those familiar with the key differences between the versions. This is a fantastic entry point for anyone looking to dive into AI or Data Science; its hands-on, beginner-friendly approach, rather than getting bogged down in theory, makes it a breeze to follow and genuinely enjoyable. Experienced individuals won't find much new here, and the implementations aren't exactly production-ready, but for newcomers, it perfectly lives up to its 'Data Science from Scratch' promise.
This book really shines in its explanation of fundamental data science concepts and popular machine learning models. While the author intentionally includes code examples for building things from scratch, which can be a bit much, it's crucial for understanding how libraries like pandas and scikit-learn actually work. Without this foundational knowledge, it's easy to just use the tools without truly appreciating the underlying mechanics.
This book is a solid starting point for data analysts venturing into statistical analysis and machine learning, offering a practical introduction to many key concepts. While it excels at providing a hands-on approach, those seeking a deeper understanding of statistical learning principles might find it a bit light on theory. It's a shame the MapReduce section feels a bit dated, and the coverage of NLP and foundational non-ML statistics like time series and frequentist methods could have been more robust. For a more in-depth dive into statistical learning, you'd be better served by resources like IPSUR or ISLR.
This book really dives into how crucial data has become in the modern business landscape. It highlights the constant influx of information from various digital sources and the exciting new technologies emerging to manage it all. The author makes a compelling case for data science being the key to unlocking insights and predicting future trends, positioning data scientists as essential figures for the coming era.
This book offers a swift introduction to Data Science, though it's not particularly insightful for experienced practitioners. While the "from Scratch" approach to implementing algorithms in Python is interesting, it feels largely unnecessary given the prevalence of efficient, pre-built libraries. The author does point readers toward more practical tools, but I wish more emphasis had been placed on those resources. It's a decent starting point for a quick overview, but serious learners should definitely seek out more hands-on guides afterward.
This book is an absolute gem for anyone wanting to get into data science. Joel Grus really nails it by focusing on first principles and using Python, which just makes so much sense. You'll get a solid grasp of the fundamentals, and the way he balances theory with practical application is fantastic – you're building things from the get-go. It's written so clearly, even the tricky stuff feels manageable, and the code examples are super helpful for cementing what you've learned. Honestly, it's a great read whether you're just starting out or looking to sharpen your skills.
This book is a fantastic introduction to data science concepts, really clarifying what the field entails. The inclusion of code examples effectively reinforces the material and even taught me some new Python techniques. I'll definitely be keeping this handy as a reference for core ideas and algorithm implementations, as the information presented is incredibly robust. While it can be challenging to read straight through, Joel's engaging writing style and subtle humor make it an enjoyable experience, especially for newcomers like myself.
Joel Grus' "Introduction to Data Science" is a truly fantastic resource, especially for beginners looking to grasp the core concepts and techniques. The book is exceptionally well-organized, making even complex topics like linear algebra, which is crucial for operations research, feel accessible and practical. Grus does a superb job of presenting current trends and technologies, offering valuable insights into the evolving data science landscape. It's more than just an introductory text; it's a guide that fosters a data-driven mindset and bridges communication gaps, making it an indispensable tool for anyone aiming to succeed in today's data-rich world.
This book offers a solid overview of data science, though it's definitely geared towards those with existing Python experience, making it feel more like a software engineer's guide. The humor often falls flat, and the inclusion of hard-coded links feels a bit dated, but it serves as a decent refresher on the core concepts.
This book is a fantastic entry point into the world of machine learning. While you might not become an expert overnight, you'll gain a broad understanding of many ML concepts. The author cleverly uses Python code to illustrate algorithms rather than lengthy explanations, showcasing elegant and compact implementations that offer deeper insight than traditional descriptions. Each chapter concludes with helpful 'where to go next' sections, guiding you toward further learning in areas like Bayesian classifiers and natural language processing. The writing is engaging, making the reading experience as enjoyable as the code itself, and the author's infectious enthusiasm is sure to inspire aspiring ML practitioners.
This book offers a solid overview of the subject matter. However, it could have been even better with more specific details on which software packages actually incorporate the discussed functionalities.
This book was surprisingly entertaining for a programming text, managing to keep the content engaging without sacrificing too much depth. While some examples felt a bit too basic to truly showcase the methods' utility, the overall experience was fun and informative. I did find the lack of equations and the necessity of reading Python to grasp the fundamentals a bit frustrating, particularly in the initial chapters.
This book offers a solid introduction to a variety of topics, providing plenty of practical examples. While the code's lack of library reliance might limit its real-world application, it's invaluable for grasping fundamental concepts and understanding the underlying mechanisms. Plus, the inclusion of humor makes the reading experience quite enjoyable.
This book really missed the mark for me. It seems to assume readers either already know data science or don't, and it fails to serve either audience effectively. If you're already in the field, the explanations are too rudimentary and the code examples, which avoid standard libraries, are largely unhelpful. For newcomers, the concepts are explained too superficially, and the 'from scratch' code is difficult to follow and impractical for real-world use. Ultimately, it feels more like a 'Python from scratch' guide for poorly explained data science concepts rather than a true introduction to data science from the ground up. While not entirely without merit, it's certainly not a recommended starting point.
This book occupies an odd space, attempting to bridge theory and practice without fully committing to either. While the author's commitment to building everything from scratch results in some elegant and Pythonic code, it also means the explanations for complex models can feel overly simplified and frustrating. It's a shame that some crucial topics, like neural networks, are covered so briefly that they're likely to leave the average reader confused. Ultimately, this is best suited for intermediate practitioners looking to solidify their understanding of fundamental methods, rather than beginners.
This book offers a solid, hands-on introduction to various concepts, complete with clear, step-by-step guidance for building functional code. While I appreciated the practical approach, I was hoping for a bit more in-depth mathematical and theoretical exploration, which is why it earned a respectable four stars rather than a perfect five.
This book offers a delightful exploration of current data science themes, presented in a remarkably accessible textbook style. While it might skim over rigorous proofs, it compensates beautifully by guiding readers through the construction of simplified models for key concepts. For someone like me, new to data science but comfortable with Python and mathematics, it proved to be an incredibly valuable read. I'm now eagerly anticipating a sequel that delves into these same topics using the industry-standard libraries.
This book offers a decent introduction to data science with Python. However, I'm starting to feel like we have an overabundance of introductory texts on data science, whether they use R or Python. Many of them are beginning to feel quite similar, which makes it hard for new ones to stand out.
This book is a great resource for understanding the fundamental concepts behind more advanced programming. While many tasks can be simplified with modern libraries, diving into this text provides crucial insight into the 'why' and the foundational building blocks of complex applications.
This book is a fantastic introduction for beginners! Instead of just telling you to import common libraries, it guides you through building them from scratch. While the custom implementations are inefficient for real-world use, they offer an invaluable peek under the hood of how these tools function. The author clearly explains the computations within the code comments, making it easy to follow the step-by-step process.
This book offers a nice refresher, particularly with its practice exercises and straightforward language. However, it falls short as a comprehensive study guide due to some missing details in certain sections, outdated code examples, and apps that aren't always practical. While it's a pleasant read, don't rely on it for in-depth learning.
This book serves as a solid introduction to data science, making it an excellent choice for anyone looking to grasp the fundamentals through practical application. It's definitely a go-to for beginners wanting a hands-on learning experience.
