# Machine Learning by Tom M. Mitchell

This textbook provides a single source introduction to the primary approaches to machine learning. It is intended for advanced undergraduate and graduate students, as well as for developers and researchers in the field. No prior background in artificial intelligence or statistics is assumed. Several key algorithms, example date sets and project- oriented home work assignments discussed in the book are accessible through the World Wide Web.Feature:

- The book covers the concepts and techniques from the various fields in a unified fashion
- Covers very recent subjects such as genetic algorithms, re-enforcement learning and inductive logic programming.
- Writing style is clear, explanatory and precise

# Machine Learning using Python by Manaranjan Pradhan

This book is written to provide a strong foundation in Machine Learning using Python libraries by providing real-life case studies and examples. It covers topics such as Foundations of Machine Learning, Introduction to Python, Descriptive Analytics and Predictive Analytics. Advanced Machine Learning concepts such as decision tree learning, random forest, boosting, recommender systems, and text analytics are covered. The book takes a balanced approach between theoretical understanding and practical applications. All the topics include real-world examples and provide step-by-step approach on how to explore, build, evaluate, and optimize machine learning models.

# Business Analytics: The Science of Data – Driven Decision Making by U Dinesh Kumar (Author)

The book has 17 chapters and addresses all components of analytics such as descriptive, predictive and prescriptive analytics. The first few chapters are dedicated to foundations of business analytics. Introduction to business analytics and its components such as descriptive, predictive and prescriptive analytics along with several applications are discussed in Chapter 1. In Chapters 2 to 8, we discuss basic statistical concepts such as descriptive statistics, concept of random variables, discrete and continuous random variables, confidence interval, hypothesis testing, analysis of variance and correlation. Chapters 9 to 13 are dedicated to predictive analytics techniques such as multiple linear regression, logistic regression, decision tree learning and forecasting techniques. Clustering is discussed in Chapter 14. Chapter 15 is dedicated to prescriptive analytics in which concepts such as linear programming, integer programming, and goal programming are discussed. Stochastic models and Six Sigma are discussed in Chapters 16 and 17, respectively.

# Approaching (Almost) Any Machine Learning Problem :by Abhishek

This is not a traditional book.

The book has a lot of code. If you don’t like the code first approach do not buy this book. Making code available on Github is not an option.

This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book doesn’t explain the algorithms but is more oriented towards how and what should you use to solve machine learning and deep learning problems. The book is not for you if you are looking for pure basics. The book is for you if you are looking for guidance on approaching machine learning problems. The book is best enjoyed with a cup of coffee and a laptop/workstation where you can code along.

Table of contents:

– Setting up your working environment

– Supervised vs unsupervised learning

– Cross-validation

– Evaluation metrics

– Arranging machine learning projects

– Approaching categorical variables

– Feature engineering

– Feature selection

– Hyperparameter optimization

– Approaching image classification & segmentation

– Approaching text classification/regression

– Approaching ensembling and stacking

– Approaching reproducible code & model serving

There are no sub-headings. Important terms are written in bold.

I will be answering all your queries related to the book and will be making YouTube tutorials to cover what has not been discussed in the book. To ask questions/doubts, please create an issue on github repo: https: //github.com/abhishekkrthakur/approachingalmost

And Subscribe to my youtube channel: https: //bit.ly/abhitubesub

# Machine Learning for Beginners: Learn to Build Machine Learning Systems Using Python (English Edition) BY Harsh Bhasin

This book covers important concepts and topics in machine learning. It begins with data cleansing and presents an overview of feature selection. It then talks about training and testing, cross-validation, and feature selection. The book covers algorithms and implementations of the most common feature selection techniques. The book then focuses on linear regression and gradient descent. Some of the important classification techniques such as K-Nearest neighbors, logistic regression, naïve Bayesian, and linear Discriminant analysis are covered in the book. It then gives an overview of neural networks and explains the biological background, the limitations of the perceptron, and the backpropagation model. The support vector machines and kernel methods are also included in the book. It then shows how to implement decision trees and random forests.

# Applied Machine Learning by Madan GopaL

Very good book for developing the theoretical basis of machine learning. The approach is mathematical and demands a graduate level mathematical understanding. It covers statistical learning, ANN and genetic algorithm also.

# Real-World Machine Learning Paperback : by Henrik Brink

Machine learning systems help you find valuable insights and patters in data which you had never recognized in the traditional methods. In the real world, ML techniques give you a way to identify trends, forecast behavior and make fact-based recommendations. It’s a hot and growing field and up-to speed ML developers are in demand. Real-World Machine Learning will teach you the concepts and techniques you need to be a successful machine learning practitioner without overdosing you on abstract theory and complex mathematics. By working through immediately relevant examples in Python, you’ll build skills in data acquisition and modelling, classification and regression.

# Machine Learning | First Edition | By Pearson Paperback by Saikat Dutt (Author), Subramanian Chandramouli (Author), Amit Kumar Das (Author)

Table of Contents:

1 Introduction to Machine Learning

2 Preparing to Model

3 Modelling and Evaluation

4 Basics of Feature Engineering

5 Brief Overview of Probability

6 B ayesian Concept Learning

7 Super vised Learning: Classification

8 Super vised Learning: Regression

9 Unsupervised Learning

10 Basics of Neural Network

11 Other Types of Learning

# Machine Learning by Anuradha Srinivasaraghavan (Author), Vincy Joseph (Author)

This book offers the readers The basics of machine learning in a very simple, user-friendly language. While browsing the table of Contents, you will realize that you are given an introduction to every concept that comes under the umbrella of machine learning. This book is aimed at students who are new to the topic of machine learning. It is meant for students studying machine learning in their undergraduate and postgraduate courses in information Technology. It is also aimed at computer engineering students. It will help familiarize students with the Terms and terminologies used in machine learning. We hope that this book serves as an entry point for students to pursue their future studies and careers in machine learning.

# Machine Learning with Python for Everyone by Pearson : by Mark Fenner (Author)

Students are crushing to master powerful machine learning techniques for improving decision-making and scaling analysis to immense datasets. Machine learning with Python for everyone brings together all they’ll need to succeed: a practical understanding of the machine learning process, accessible code, skills for implementing that process with Python and the scikit-learn library, and real expertise in using learning systems intelligently.Reflecting 20 years of experience teaching non-specialists, the author teaches through carefully-crafted datasets that are complex enough to be interesting, but simple enough for non-specialists. Building on this foundation, the book presents real-world case studies that apply his lessons in detailed, nuanced ways. Throughout, he offers clear narratives, practical “code-alongs,” and easy-to-understand images — focusing on Mathematics only where it’s necessary to make connection and deepen insight.

table of Contents:

Chapter 1: Let’s discuss learning

Chapter 2: predicting categories: getting started with classification

Chapter 3: predicting numerical values: getting started with regression

Chapter 4: evaluating and comparing learners

Chapter 5: evaluating classifiers

Chapter 6: evaluating Regressors

Chapter 7: more classification methods

Chapter 8: more regression methods

Chapter 9: manual feature engineering: manipulating data for fun and Profit

Chapter 10: models that engineer features for us

Chapter 11: feature engineering for domains: domain-specific learning online chapters

Chapter 12: tuning hyperparameters and pipelines

Chapter 13: combining learners

Chapter 14: connecting, extensions, and further directions

**Understanding Machine Learning: From Theory to Algorithms Paperback **

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.