| Brief | Book | |
|---|---|---|
| This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates. |
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Saturday, August 15, 2020
Elements of Statistical Learning...Data Mining, Inference, and Prediction
Advances in Financial Machine Learning
| Brief | Book | |
|---|---|---|
| Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to back test your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance. |
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Machine Learning with Python for Everyone
| Brief | Book | |
|---|---|---|
| This book will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you’re an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. The author relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning. The discussion begins with machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you’ll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field’s most sophisticated and exciting techniques. Whether you’re a student, analyst, scientist, or hobbyist, this guide’s insights will be applicable to every learning system you ever build or use. |
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Machine Learning and Artificial Intelligence with Just Enough R!
| Brief | Book | |
|---|---|---|
Features
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Deep Learning for Coders with fastai and PyTorch
| Brief | Book | |
|---|---|---|
| Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. | ||
Data Science for Marketing:...Improve your strategies...using Python and R
| Brief | Book | |
|---|---|---|
| Regardless of company size, the adoption of data science and machine learning for marketing is witnessing an exponential rise in the industry. With this book, you'll learn to implement data science techniques to identify the factors behind the successes and failures of marketing campaigns. With the help of this guide, you'll also be able to understand and predict customer behavior, and create more effectively targeted and personalized marketing strategies. You'll first get to grips with performing simple through to advanced tasks, such as extracting hidden insights from data and using them to make smart business decisions. The book will further guide you through understanding what drives sales and increases customer engagement for your products. As you explore further chapters, you'll not only learn how to gain insights into consumer behavior using exploratory analysis, but also discover the concept of A/B testing and implement it using Python and R. |
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Grokking Machine Learning
| Brief | Book | |
|---|---|---|
| It's time to dispel the myth that machine learning is difficult. Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using readily-available machine learning tools!
In Grokking Machine Learning, expert machine learning engineer Luis Serrano introduces the most valuable ML techniques and teaches you how to make them work for you. Practical examples illustrate each new concept to ensure you’re grokking as you go. You’ll build models for spam detection, language analysis, and image recognition as you lock in each carefully-selected skill. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert. |
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