Bests & Worsts Reviews from Amazon

First some general, higher-level thoughts and comments before I dive into specifics: MY BACKGROUND: Data Scientist; B.S. in Economics and M.S. in Business Analytics; experienced (though by no means expert) user of Scikit-learn OVERALL THOUGHTS: I've purchased and read (virtually) every Machine Learning book that aims to teach the reader the basics of ML using the Scikit-learn library as the main focus. I've found them to be...less than satisfactory. The examples in other books often use ML techniques in contexts for which they are not intended to be used and/or contexts they are not used in out in the real world (among other issues I have found within them). In stark contrast, Python Machine Learning by Sebastian Raschka is stunningly-impressive, not only for the breadth and depth of coverage, but also in the manner the information is presented to the reader. To date, I have not encountered a book on ML that incorporates multiple levels of learning in a manner such as this. It is the textual equivalent of a Neural Network with hundreds of hidden layers running on the latest NVIDIA GPU (if that comparison is lost on you, don’t worry; it’ll all make sense by the time you finish the book). One of the underlying (though understated) themes in the book is the importance of using visual aids where appropriate to gauge the performance of the algorithms you’re using as well as to understand exactly what is going on behind the scenes, so-to-speak. If you’re a novice user of the Matplotlib graphics library for Python, this book will greatly improve your visualization skills by the time you’re done which I found to be an added bonus. Another underlying theme is basic optimization using the NumPy library. This is reinforced throughout the book in the examples that you code by hand. Ditto for the Pandas library. To those of you brand-new to Python, you may not fully appreciate this aspect of the book until you gain some more experience and you’ve gone through the book a few times. For those of you who are more experienced users, the examples provide an amazing amount of insight into simple ways to make your code more efficient. Indeed, “Best Practices” abound in Python Machine Learning. As a final general thought, Sebastian is an active contributor to Scikit-learn; something I do not believe to be the case with the authors of the other books that I’ve read. In order to effectively demonstrate and communicate the power of the Scikit-learn library, you really need to be familiar with it from a fundamental level. Sebastian has this knowledge in spades and that becomes readily apparent as you progress through the book. He makes no assumption about the knowledge base of the reader—he doesn’t have to—because the book incorporates learning styles appropriate for differing skill levels (see below) FOR BEGINNING USERS: You may have some experience with Scikit-learn and Python, though not necessarily enough where you have developed some of the “best practices” I mentioned above; you’re still getting comfortable using the library and the Python environment. This book is definitely for you! The best way to learn this subject is by coding examples. You could not ask for a finer book on the subject; for those just starting their ML journey, you’re in good hands with Sebastian. You’ll get an excellent, hands-on education using some of the most important ML algorithms in use today in the most popular ML library used in Python. You’ll begin to develop good habits and you’ll see from a basic level, how to actually create algorithms on your own, outside of Scikit-learn! Then after you’ve had the experience of coding the algorithm by hand, you’ll move to Scikit-learn and get even more hands-on experience. You will learn about the tried-and-true algorithms that have been around for decades as well as concepts that are still in their infancy and are considered the current state-of-the-art. And as I said, you’ll learn about them by actually using them to build ML models. You’ll see how you take a concept for a project and turn it into reality using some really fantastic algorithms. FOR INTERMEDIATE USERS: You are comfortable using Python and Scikit-learn and have participated (or you are considering participating) in one or more Kaggle competitions. You may have some good habits/best practices formed but you’re looking to take the next step; you may know how to take a project from the data gathering and cleaning stage to a final model, but you may not have actually done it or you aren’t sure how to properly evaluate the model you have created in the end stages; you want to gain a thorough understanding of which situations are appropriate for each of the algorithms and more importantly, which situations are NOT appropriate for each of the algorithms; you want to gain a firm knowledge of how the algorithms work and you’re curious about what the state-of-the-art concepts are. Good news! This book is DEFINITELY for you. There comes a time in every Data Scientist’s life when you have read everything you can find on how to structure and complete projects and you feel confident that you’re ready. Then you start and you realize during the course of a project that you suddenly have a dozen more questions: What should I do with all of these missing values? Should I use PCA and/or other dimensionality reduction techniques? How many folds should I use in my Cross Validation? Should I use Nested Cross Validation or will simple K-Fold Cross Validation suffice? Do I need to standardize my data in order to use run a Logistic Regression algorithm? How about with a Random Forest? What performance metric is most appropriate for my model? What is L1 and L2 regularization (again) and when should I use it? If you have ever asked yourself any of these questions, rest assured this book will become your go-to reference for these questions as well as questions that you haven’t even thought of yet. Sebastian will fill in the gaps in your knowledge and you’ll gain the confidence to tackle the projects you have been looking forward working on all this time. FOR ADVANCED USERS: Much of the information in this book may be familiar to you, however the mathematical concepts behind the algorithms may not be. You may be interested in reading the seminal research on each of the concepts presented in the book. Sebastian has you covered as well. He provides symbolic mathematical proofs for those so-inclined, as well as a multitude of citations for where you can find the research that supports and/or explores the concepts more thoroughly. The book is well-researched and cited and the concepts are given very thorough treatment. TL;DR (SUMMARY): I realize the experience levels described above are subjective. They are present merely to serve as reference points for the readers and to underscore my belief that Python Machine Learning has something for virtually every skill level. I cannot recommend this book more highly! BONUS - Topics/Algorithms Covered Throughout the Book (there are a TON!): Perceptron Adaline Stochastic Gradient Descent (SGD) Support Vector Machines (SVM) Logistic Regression Difference between L1 and L2 regularization (with excellent graphics showing the difference) Out-of-core/online learning (truly Big Data) The Kernel Trick Parameter Optimization Random Forest Classifier Parametric vs. Non-parametric (and which are which) K-Nearest Neighbors (KNN) Bias/Variance Trade off (great graphics showing the difference) Decision Trees Standardization (Data Preprocessing) Scaling (Data Preprocessing) Correct Mapping of data types for use in Scikit-learn algorithms Over/Under fitting Sequential Forward Selection (Feature Selection) Sequential Backward Selection (Feature Selection) Feature Importances using Random Forests (Feature Selection) Common pitfalls (“gotchas”) that can arise with use of Random Forests Principal Component Analysis (PCA) Latent Discriminant Analysis (LDA – this topic is almost never covered in similar books) Kernel PCA + caveats for its use Use of Pipelines in Scikit-learn for streamlining the modeling process (this never gets coverage and is a big efficiency boost) Cross Validation (K Fold) Nested Cross Validation Common Metrics for Model Evaluation and how to graph each to gauge performance Ensemble Methods (Majority Voting Classifier) Plotting Decision Boundaries (important for gaining insight into ensemble performance) Bootstrap Aggregating (Bagging) Boosting Sentiment Analysis using bag-of-words model Sentiment Analysis using SGD Classifier and Out-of-Core learning to analyze large document datasets via streaming/mini-batching for Data that is too large to fit in memory at once Embedding Machine Learning algorithms into web applications using the web framework called Flask—this is a hot skill to have in the job market Regression Analysis for Continuous Target Variables Aesthetic adjustments/extensions to Matplotlib graphs using the Seaborn library RANSAC Regression Dealing with non-linear relationships in the context of Regression (Data Transformations) Clustering (K-Means, Agglomerative, Divisive) Visualizing clusters Hard vs. Soft clustering The “Elbow” method for clustering DBSCAN clustering Common “gotchas” to be aware of when using clustering algorithms Artificial Neural Networks (ANN) Multi-Layer Perceptron (MLP) Neural Net Forward Propagation Backward Propagation Using the Theano library to run Neural Networks on Graphical Processing Units (GPU)—this is an extremely hot topic and demonstrates the timelessness of many of these algorithms.