Chapter 1, Journey from Statistics to Machine Learning, introduces you to all the necessary fundamentals and basic building blocks of both statistics and machine learning. All fundamentals are explained with the support of both Python and R code examples across the chapter.

Chapter 2, Parallelism of Statistics and Machine Learning, compares the differences and draws parallels between statistical modeling and machine learning using linear regression and lasso/ridge regression examples.

Chapter 3, Logistic Regression Versus Random Forest, describes the comparison between logistic regression and random forest using a classification example, explaining the detailed steps in both modeling processes. By the end of this chapter, you will have a complete picture of both the streams of statistics and machine learning.

Chapter 4, Tree-Based Machine Learning Models, focuses on the various tree-based machine learning models used by industry practitioners, including decision trees, bagging, random forest, AdaBoost, gradient boosting, and XGBoost with the HR attrition example in both languages.

Chapter 5, K-Nearest Neighbors and Naive Bayes, illustrates simple methods of machine learning. K-nearest neighbors is explained using breast cancer data. The Naive Bayes model is explained with a message classification example using various NLP preprocessing techniques.

Chapter 6, Support Vector Machines and Neural Networks, describes the various functionalities involved in support vector machines and the usage of kernels. It then provides an introduction to neural networks. Fundamentals of deep learning are exhaustively covered in this chapter.

Chapter 7, Recommendation Engines, shows us how to find similar movies based on similar users, which is based on the user-user similarity matrix. In the second section, recommendations are made based on the movie-movies similarity matrix, in which similar movies are extracted using cosine similarity. And, finally, the collaborative filtering technique that considers both users and movies to determine recommendations, is applied, which is utilized alternating the least squares methodology.

Chapter 8, Unsupervised Learning, presents various techniques such as k-means clustering, principal component analysis, singular value decomposition, and deep learning based deep auto encoders. At the end is an explanation of why deep auto encoders are much more powerful than the conventional PCA techniques.

Chapter 9, Reinforcement Learning, provides exhaustive techniques that learn the optimal path to reach a goal over the episodic states, such as the Markov decision process, dynamic programming, Monte Carlo methods, and temporal difference learning. Finally, some use cases are provided for superb applications using machine learning and reinforcement learning.

This book assumes that you know the basics of Python and R and how to install the libraries. It does not assume that you are already equipped with the knowledge of advanced statistics and mathematics, like linear algebra and so on.

The following versions of software are used throughout this book, but it should run fine with any more recent ones as well:

This book is intended for developers with little to no background in statistics who want to implement machine learning in their systems. Some programming knowledge in R or Python will be useful.

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning. Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "The mode function was not implemented in the numpy package.". Any command-line input or output is written as follows:

>>> import numpy as np 
>>> from scipy import stats 
>>> data = np.array([4,5,1,2,7,2,6,9,3]) 
# Calculate Mean 
>>> dt_mean = np.mean(data) ; 
print ("Mean :",round(dt_mean,2)) 

New terms and important words are shown in bold.

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