What are the top Machine Learning books to read for career changers?

I recommend the following books in this specific order: - "Introduction to Machine Learning" from Kaggle: This website is an excellent starting point, offering practical examples to help grasp the basics of machine learning. - "Probabilistic Machine Learning: An Introduction" by Kevin Murphy: I highly recommend this book for a deeper dive into the probabilistic aspects of machine learning. - "Deep Learning" by Ian Goodfellow: To explore the world of deep learning, this book by Ian Goodfellow is an invaluable resource. - "Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications": Once gained a solid foundation, this book will guide you in creating production-ready machine learning applications.

For hands on content there is nothing better than Francois Chollet’s book: Deep Learning with Python. It’s all in Keras, but you can always use ChatGPT to learn it using PyTorch in parallel.

"Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurelien Geron is a highly popular and practical book in the field of machine learning. This book equips readers with the skills to effectively utilize essential Python libraries like Scikit-Learn, Keras, and TensorFlow. It covers a wide range of machine learning models, including regression, classification, clustering, neural networks, and deep learning. Additionally, the book delves into crucial concepts and techniques such as data preprocessing, feature engineering, model evaluation, hyperparameter tuning, and deployment. Through a wealth of examples, exercises, and valuable tips, it offers a hands-on approach to mastering machine learning.

I have learned a lot from these 2 books. Deep learning for coders with Fastai & PyTorch by Jeremy Howard & Sylvain Gugger is a fantastic resource for less-known technical details and deep dives. Natural Language Processing with Transformers by Lewis Tunstall, Leandro von Werra, Thomas Wolf is a great start for folks serious about NLP.

My top 3 are: 1. An Introduction to Statistical Learning - Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani 2. Mathematics for Machine Learning - Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. Published by Cambridge University Press. 3. Algorithms for Decision Making - Mykel J. Kochenderfer, Tim A. Wheeler, and Kyle H. Wray MIT Press, 2022

Undoubtely I would read Probabilistic Machine Learning, an introduction by Murphy if you are technical. It is a hard book but it is the necessary knowledge to start with. Also more old but gold Pattern Recognition by Christopher Bishop. Those two books are the pure methodology behind machine learning, no how to code it.

"Pattern Recognition and Machine Learning" by Christopher Bishop is a classic and extensive resource for those seeking a deeper understanding of the mathematical and statistical foundations of machine learning. This book comprehensively covers essential topics in machine learning, including supervised and unsupervised learning, Bayesian methods, graphical models, neural networks, and kernel methods. It offers a rigorous and elegant treatment of machine learning theory and algorithms, supported by numerous examples and exercises. This book is ideal for advanced learners looking to gain a profound grasp of the principles and derivations that underlie machine learning.

Pattern Recognition and Machine Learning by Christopher M. Bishop is another must-read for aspiring data scientists and machine learning enthusiasts. This book delves into the foundations of machine learning and pattern recognition, offering a comprehensive overview of the field. Bishop provides in-depth explanations of various machine learning algorithms, including neural networks and support vector machines, along with practical insights on how to apply these techniques. What sets this book apart is its blend of mathematical rigor and intuitive explanations, making complex topics more accessible to a wide range of readers. It's a valuable resource for those who want a deeper understanding of machine learning.

Grokking Algorithms - Aditya Y. Bhargava another excellent book where visual representation of various algorithms can make learning about algorithm and also touches on advanced topics and algorithms. A good refresher for experienced developers,

Pattern Recognition and Machine Learning by Christopher M. Bishop: This book is more mathematical but provides a deep understanding of the fundamentals of machine learning and pattern recognition.

"Machine Learning: A Probabilistic Perspective" by Kevin Murphy offers an excellent exploration of the probabilistic approach to machine learning. It presents a unified and contemporary framework for understanding machine learning by emphasizing the modeling of data using probability distributions and making inferences through Bayesian methods. This comprehensive book addresses various topics, including linear and nonlinear models, graphical models, latent variable models, inference algorithms, deep learning, reinforcement learning, and more. To enhance understanding, the book includes numerous code examples, figures, and exercises that demonstrate the practical applications and concepts of machine learning.

Machine Learning: A Probabilistic Perspective by Kevin P. Murphy is a fantastic read for those eager to grasp the core concepts of machine learning. It offers a unique focus on understanding the role of probability in machine learning, which is essential for anyone stepping into this field. One key feature is its ability to demystify complex topics. The author's explanations are clear and engaging, helping readers see how probabilistic reasoning is applied to various machine learning algorithms. With real-world examples, it becomes easier to connect theory to practice.

I have some interesting new finds for this 1.Causal Inference and Discovery in Python by Alekshander Molak, amazing book, great new narrative on causality, highly recommended. 2.Bayesian Analysis with Python by Osvaldo Martin, this book is a bit dated, however, covers some great topics. 3.Machine Learning: A Probabilistic Perspective by Kevin P. Murphy another gem in my list.

Python Machine Learning provides practical examples and real-world applications, making it an ideal resource for both beginners and those looking to apply machine learning to actual projects and scenarios.

Python Machine Learning is a fantastic book for those switching to a career in data science. It provides hands-on guidance and covers various machine learning concepts using Python, making it an ideal choice for career changers.

"Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili is a recommended choice for those seeking a beginner-friendly and concise introduction to machine learning using Python. This book covers fundamental concepts such as data manipulation, visualization, feature extraction, model selection, evaluation, and optimization. It introduces readers to popular and powerful machine learning algorithms like linear and logistic regression, decision trees, random forests, support vector machines, k-means clustering, and principal component analysis. Moreover, the book demonstrates how to utilize Python libraries including NumPy, pandas, matplotlib, scikit-learn, and TensorFlow to implement and evaluate machine learning models.

Yes, no doubt it is great to start the Python journey for data science. It will not teach you Python Programming Language itself. You should be ready with your Python knowledge. Apart from that you need to have the ML algorithmic knowledge in your bucket. Only you will find it useful.

Python Machine Learning by Sebastian Raschka and Vahid Mirjalili: This book is a great starting point for those new to both machine learning and Python. It covers the basics of machine learning and provides practical examples. Machine Learning Yearning by Andrew Ng: This book is available for free as part of Andrew Ng's deeplearning.ai course, and it focuses on practical strategies for developing machine learning projects. Introduction to Machine Learning with Python by Andreas C. Müller & Sarah Guido: This book is a hands-on introduction to machine learning with Python. It covers essential concepts and includes practical examples using popular libraries like scikit-learn.

Machine Learning for Hackers is a highly recommended book for those transitioning into the field of machine learning. It offers practical insights and techniques, which are invaluable for career changers.

"Machine Learning for Hackers" by Drew Conway and John Myles White is a valuable resource for hackers, coders, and data enthusiasts aiming to learn machine learning through practical application. This book guides readers in using machine learning to tackle real-world challenges, including tasks like spam filtering, sentiment analysis, recommendation systems, and text mining. It highlights the use of the R programming language and its associated packages such as ggplot2, plyr, and caret for data manipulation, visualization, and model building. The book offers an engaging and enjoyable learning experience, assuming a basic understanding of programming and statistics.

This is one of my favourtive areas and interesting books that overlap Machine Learning and Cybersecurity. 1.Machine Learning for Cybersecurity Cookbook by Emmanuel Tsukerman. The author is super talented and highly revered to in the community, a great desk reference guide. 2.10 Machine Learning Blueprints You Should Know for Cybersecurity by Rajvardhan Oak- an interesting view on the current landscape, give it a peak. 3.Machine Learning for Hackers by Drew Conway and John Myles White is undoubtedly a gem.

"Weapons of Math Destruction" by Cathy O'Neil is a thought-provoking exploration of the social and ethical dimensions of machine learning. This book sheds light on how machine learning algorithms can have detrimental and unfair impacts on individuals and society, including issues such as discrimination against minorities, the reinforcement of biases, privacy infringements, and threats to democratic processes. O'Neil also underscores the opacity, lack of accountability, and absence of regulation surrounding these algorithms, highlighting how they can perpetuate inequality and injustice through feedback loops.

Mathematics and Statistics are the pillars of ML and DS apart from the renowned books, I'd like to suggest some hidden gems 1.Hands-On Mathematics for Deep Learning by Jay Dawani, this book has it's raw edges which could do with some polish, but at the same time it has great value for readers from different arenas and skill levels. 2.Practical Discrete Mathematics by Ryan T. White (Author), Archana Tikayat Ray (Author) Another gem, this book is very popular among university students and professionals alike. 3.The Statistics and Machine Learning with R Workshop by Peng Liu PhD- I am a sucker for Workshop titles and this one is an interesting take on R for stats and ML. The author does a great job with brain teasers and real world egs.

Weapons of Math Destruction an aboslute must non-technical book if you want to excite people on machine learning space. The books provides lot of examples where situations gone wrong when people utilize the tools of data science without a firm conceptual understanding of their roles and responsibilities Everybody Lies by Seth Stephens would be another recommendation where the author pulls startling contrast between what people publicly post on social media and what they search for when they naively assume that nobody is watching.

I recommend ISLR((An Introduction to Statistical Learning) to learn the basics of statistics that would benefit once you start implementing ML models.

My go-to list of books to refer to are as follows - 1. The Deep Learning Book by Ian Goodfellow : It starts off with the required pre-requisites such as Linear Algebra, Probability, etc., and then moves on to the foundations of Machine Learning. The second half of the book is all about different models in Deep Learning. 2. Pattern Recognition - by Duda Hart Stork: A textbook bible (more mathematically rigorous) that I've used in university. 3. Machine Learning - by Mitchell: For more serious researchers, for an advanced understanding of ML.

I would recommend the following books: - Design machine learning system - Deep Learning (Adaptive Computation and Machine Learning Series)

Machine Learning with R by Brett Lantz for understanding the basic concepts better. The heavily updated 4th edition just came out. The book explains a lot of basic ML alhorithms in a way that is easy to understand even without a strong math/CS background. The example code is in R, but I can recommend this book even for Python users as it’s easy to translate the code once you understand the concepts. I did the same within R. The code is in the base R ecosystem using individual ML packages and I translated it to match the tidyverse/tidymodels frameworks.

I highly recommend "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: For those interested in deep learning, this book is an amazing resource that covers both theory and practical applications. If you want a short and introductory one - "The Hundred-Page Machine Learning Book" by Andriy Burkov is a concise, easy-to-understand guide to machine learning concepts and techniques.