Book Portfolio

Book Portfolio

Presenting to you a consolidated view of the books I published as an Author, Contributing Author and Tech Reviewer.

Solely Authored Books

Applied Supervised Learning with R

Use ML libraries of R to build models that solve problems and predict business trends

Authors: Moolayil, Jojo & Ramasubramanian, Karthik 

What is the book about?
Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras.

What is this book about?

Supervised Learning with R covers the complete process of developing applications with supervised machine learning algorithms that cater to your business needs. Your learning curve starts with developing your analytical thinking towards creating a problem statement using business inputs or domain research. You will learn many evaluation metrics that compare various algorithms, and you can then use these metrics to select the best algorithm for your problem. After finalizing the algorithm you want to use, you will study the hyperparameter optimization technique to fine tune your set of optimal parameters. To avoid overfitting your model, you will also be shown how to add various regularization terms.

When you complete the book, you will find yourself an expert at modelling a supervised machine learning algorithm that precisely fulfills your business need.

What will you learn?

Develop analytical thinking to precisely identify a business problem. Wrangle data with dplyr, tidyr, and reshape2. Validate your supervised machine learning model using k-fold. Optimize hyperparameters with grid and random search and bayesian optimization. Deploy your model on AWS Lambda with plumber, an R package. Improve the model performance with feature selection and dimensionality reduction. Use a research paper with a real-world problem, frame a problem statement, and replicate its results with a supervised machine learning model

Who This Book Is For?  SResearchers, Academics, Software engineers and data engineers with basic programming skills in any language and who are keen on exploring deep learning for a career move or an enterprise project.

Learn Keras for Deep Neural Networks

A Fast-Track Approach to Modern Deep Learning with Python

Authors: Moolayil, Jojo 
Tech Reviewer: Swamynathan, Manohar  
ISBN 9781484242407

What is the book about?
Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras.

The overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. You’ll tackle one use case for regression and another for classification leveraging popular Kaggle datasets. 

Later, you will see an interesting and challenging part of deep learning: hyperparameter tuning; helping you further improve your models when building robust deep learning applications. Finally, you’ll further hone your skills in deep learning and cover areas of active development and research in deep learning. 

At the end of Learn Keras for Deep Neural Networks, you will have a thorough understanding of deep learning principles and have practical hands-on experience in developing enterprise-grade deep learning solutions in Keras.

What You’ll Learn?  Master fast-paced practical deep learning concepts with math- and programming-friendly abstractions.Design, develop, train, validate, and deploy deep neural networks using the Keras frameworkUse best practices for debugging and validating deep learning modelsDeploy and integrate deep learning as a service into a larger software service or productExtend deep learning principles into other popular frameworks

Who This Book Is For?  Software engineers and data engineers with basic programming skills in any language and who are keen on exploring deep learning for a career move or an enterprise project.

Smarter Decisions – The Intersection of IoT & Decision Science

Enter the world of Internet of Things with the power of data science with this highly practical, engaging book

Authors: Moolayil, Jojo 
Tech Reviewer: Basak, Anindita  
Purchase now
ISNN  9781785884191

What is the book about? 

With an increasing number of devices getting connected to the Internet, massive amounts of data are being generated that can be used for analysis. This book helps you to understand Internet of Things in depth and decision science, and solve business use cases. With IoT, the frequency and impact of the problem is huge. Addressing a problem with such a huge impact requires a very structured approach.

The entire journey of addressing the problem by defining it, designing the solution, and executing it using decision science is articulated in this book through engaging and easy-to-understand business use cases. You will get a detailed understanding of IoT, decision science, and the art of solving a business problem in IoT through decision science.

By the end of this book, you’ll have an understanding of the complex aspects of decision making in IoT and will be able to take that knowledge with you onto whatever project calls for it

What You Will Learn Explore decision science with respect to IoTExplore decision science with respect to IoT. Get to know the end to end analytics stack – Descriptive + Inquisitive + Predictive + Prescriptive. Solve problems in IoT connected assets and connected operations. Design and solve real-life IoT business use cases using cutting edge machine learning techniques. Synthesize and assimilate results to form the perfect story for a business. Master the art of problem solving when IoT meets decision science using a variety of statistical and machine learning techniques along with hands on tasks in R

Books Tech Reviewed

Applied Deep Learning

A Case-Based Approach to Understanding Deep Neural Networks

Authors: Michelucci, Umberto 
Tech Reviewer: Moolayil, Jojo 
ISBN 9781484237908

What is the book about? Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and Swish), seeing how to perform linear and logistic regression using TensorFlow, and choosing the right cost function.

The next section talks about more complicated neural network architectures with several layers and neurons and explores the problem of random initialization of weights. An entire chapter is dedicated to a complete overview of neural network error analysis, giving examples of solving problems originating from variance, bias, overfitting, and datasets coming from different distributions.

Applied Deep Learning also discusses how to implement logistic regression completely from scratch without using any Python library except NumPy, to let you appreciate how libraries such as TensorFlow allow quick and efficient experiments. Case studies for each method are included to put into practice all theoretical information. You’ll discover tips and tricks for writing optimized Python code (for example vectorizing loops with NumPy).

What You Will Learn? Implement advanced techniques in the right way in Python and TensorFlow
Debug and optimize advanced methods (such as dropout and regularization)
Carry out error analysis (to realize if one has a bias problem, a variance problem, a data offset problem, and so on)
Set up a machine learning project focused on deep learning on a complex dataset

Who This Book Is For?Readers with a medium understanding of machine learning, linear algebra, calculus, and basic Python programming.

Introduction to Deep Learning Business Applications for Developers

From Conversational Bots in Customer Service to Medical Image Processing

Authors:Vieira, Armando, Ribeiro, Bernardete 
Tech Reviewer: Moolayil, Jojo 

ISBN – 9781484234532

What is the book about? Discover the potential applications, challenges, and opportunities of deep learning from a business perspective with technical examples. These applications include image recognition, segmentation and annotation, video processing and annotation, voice recognition, intelligent personal assistants, automated translation, and autonomous vehicles.
An Introduction to Deep Learning Business Applications for Developers covers some common DL algorithms such as content-based recommendation algorithms and natural language processing. You’ll explore examples, such as video prediction with fully convolutional neural networks (FCNN) and residual neural networks (ResNets). You will also see applications of DL for controlling robotics, exploring the DeepQ learning algorithm with Monte Carlo Tree search (used to beat humans in the game of Go), and modeling for financial risk assessment. There will also be mention of the powerful set of algorithms called Generative Adversarial Neural networks (GANs) that can be applied for image colorization, image completion, and style transfer.
After reading this book you will have an overview of the exciting field of deep neural networks and an understanding of most of the major applications of deep learning. The book contains some coding examples, tricks, and insights on how to train deep learning models using the Keras framework.

What You Will Learn? Find out about deep learning and why it is so powerful. Work with the major algorithms available to train deep learning models.See the major breakthroughs in terms of applications of deep learning. Run simple examples with a selection of deep learning libraries.Discover the areas of impact of deep learning in business

Who This Book Is For? Data scientists, entrepreneurs, and business developers.

Deep Learning with Python

A Hands-on Introduction

Authors:Ketkar, Nikhil 
Tech Reviewer: Moolayil, Jojo 

What is the book about? 

Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms.
This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included.
Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments.

What You Will Learn Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe.  Gain the fundamentals of deep learning with mathematical prerequisites. Discover the practical considerations of large scale experiments.Take deep learning models to production

Who This Book Is For Software developers who want to try out deep learning as a practical solution to a particular problem. Software developers in a data science team who want to take deep learning models developed by data scientists to production.

Mastering Machine Learning with Python in Six Steps

A Practical Implementation Guide to Predictive Data Analytics Using Python

Authors:Swamynathan, Manohar 
Tech Reviewer: Moolayil, Jojo 
ISBN – 978-1484228661

What is the book about?

Master machine learning with Python in six steps and explore fundamental to advanced topics, all designed to make you a worthy practitioner. 
This book’s approach is based on the “Six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away. Mastering Machine Learning with Python in Six Steps presents each topic in two parts: theoretical concepts and practical implementation using suitable Python packages.

You’ll learn the fundamentals of Python programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as feature dimension reduction, regression, time series forecasting and their efficient implementation in Scikit-learn are also covered. Finally, you’ll explore advanced text mining techniques, neural networks and deep learning techniques, and their implementation.

What will you learn Examine the fundamentals of Python programming language.  Review machine Learning history and evolution.
Understand machine learning system development frameworks
Implement supervised/unsupervised/reinforcement learning techniques with examples. Explore fundamental to advanced text mining techniques.
Implement various deep learning frameworks.

Who This Book Is For  Python developers or data engineers looking to expand their knowledge or career into machine learning area. Non-Python (R, SAS, SPSS, Matlab or any other language) machine learning practitioners looking to expand their implementation skills in Python.Novice machine learning practitioners looking to learn advanced topics, such as hyperparameter tuning, various ensemble techniques, natural language processing (NLP), deep learning, and basics of reinforcement learning.

Practical Machine Learning with Python

A Problem-Solver’s Guide to Building Real-World Intelligent Systems

Authors:Sarkar, Dipanjan, Bali, Raghav, Sharma, Tushar 
Tech Reviewer: Moolayil, Jojo 

What is the book about? Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today!

What will you learn Examine the fundamentals of Python programming language.  Review machine Learning history and evolution.
Understand machine learning system development frameworks
Implement supervised/unsupervised/reinforcement learning techniques with examples. Explore fundamental to advanced text mining techniques.
Implement various deep learning frameworks.

Execute end-to-end machine learning projects and systems
Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks
Review case studies depicting applications of machine learning and deep learning on diverse domains and industries
Apply a wide range of machine learning models including regression, classification, and clustering.
Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning.

Who This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students

Machine Learning Using R

A comprehensive guide for anybody who wants to understand ML model building process end to end

Authors: Ramasubramanian, Karthik, Singh, Abhishek 
Tech Reviewer: Moolayil, Jojo 
ISBN – 9781484223345

What is the book about? 

Examine the latest technological advancements in building a scalable machine learning model with Big Data using R. This book shows you how to work with a machine learning algorithm and use it to build a ML model from raw data.

All practical demonstrations will be explored in R, a powerful programming language and software environment for statistical computing and graphics. The various packages and methods available in R will be used to explain the topics. For every machine learning algorithm covered in this book, a 3-D approach of theory, case-study and practice will be given. And where appropriate, the mathematics will be explained through visualization in R. All the images are available in color and hi-res as part of the code download.

This new paradigm of teaching machine learning will bring about a radical change in perception for many of those who think this subject is difficult to learn. Though theory sometimes looks difficult, especially when there is heavy mathematics involved, the seamless flow from the theoretical aspects to example-driven learning provided in this book makes it easy for someone to connect the dots.

What will you learn? Use the model building process flow. Apply theoretical aspects of machine learning. Review industry-based cae studies
Understand ML algorithms using R. Build machine learning models using Apache Hadoop and Spark

Who This Book Is For? Data scientists, data science professionals and researchers in academia who want to understand the nuances of machine learning approaches/algorithms along with ways to see them in practice using R. 
The book will also benefit the readers who want to understand the technology behind implementing a scalable machine learning model using Apache Hadoop, Hive, Pig and Spark.

Machine Learning and Cognition in Enterprises: 

Business Intelligence Transformed 

Authors: Kumar, Rohit 
Tech Reviewer: Moolayil, Jojo 

ISBN – 9781484230695

Learn about the emergence and evolution of IT in the enterprise, see how machine learning is transforming business intelligence, and discover various cognitive artificial intelligence solutions that complement and extend machine learning. In this book, author Rohit Kumar explores the challenges when these concepts intersect in IT systems by presenting detailed descriptions and business scenarios. He starts with the basics of how artificial intelligence started and how cognitive computing developed out of it. He’ll explain every aspect of machine learning in detail, the reasons for changing business models to adopt it, and why your business needs it.

Along the way you’ll become comfortable with the intricacies of natural language processing, predictive analytics, and cognitive computing. Each technique is covered in detail so you can confidently integrate it into your enterprise as it is needed. This practical guide gives you a roadmap for transforming your business with cognitive computing, giving you the ability to work confidently in an ever-changing enterprise environment.

What You’ll Learn? See the history of AI and how machine learning and cognitive computing evolved.  Discover why cognitive computing is so important and why your business needs it. Master the details of modern AI as it applies to enterprises.  Map the path ahead in terms of your IT-business integration. Avoid common road blocks in the process of adopting cognitive computing in your business

Who This Book Is For? Business managers and leadership teams.

Machine Learning for Decision Makers

Cognitive Computing Fundamentals for Better Decision Making

Authors: Kashyap, Patanjali 
Tech Reviewer: Moolayil, Jojo 
ISBN 9781484229880

What is the book about? Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Decision Makers serves as an excellent resource for establishing the relationship of machine learning with IoT, big data, and cognitive and cloud computing to give you an overview of how these modern areas of computing relate to each other. 
This book introduces a collection of the most important concepts of machine learning and sets them in context with other vital technologies that decision makers need to know about. These concepts span the process from envisioning the problem to applying machine-learning techniques to your particular situation. This discussion also provides an insight to help deploy the results to improve decision-making.
The book uses case studies and jargon busting to help you grasp the theory of machine learning quickly. You’ll soon gain the big picture of machine learning and how it fits with other cutting-edge IT services. This knowledge will give you confidence in your decisions for the future of your business.

What You Will Learn? Discover the machine learning, big data, and cloud and cognitive computing technology stack.  Gain insights into machine learning concepts and practices. Understand business and enterprise decision-making using machine learning. Absorb machine-learning best practices

Who This Book Is For?Managers tasked with making key decisions who want to learn how and when machine learning and related technologies can help them.

Business Analytics Using R – A Practical Approach

A Practical Approach to modern business analytics

Authors: Rao, Umesh Hodeghatta, Nayak, Umesha 
Tech Reviewer: Moolayil, Jojo 
ISBN 9781484225141

What is the book about? Learn the fundamental aspects of the business statistics, data mining, and machine learning techniques required to understand the huge amount of data generated by your organization. This book explains practical business analytics through examples, covers the steps involved in using it correctly, and shows you the context in which a particular technique does not make sense. Further, Practical Business Analytics using R helps you understand specific issues faced by organizations and how the solutions to these issues can be facilitated by business analytics.

This book includes case studies on important business analytic techniques, such as classification, association, clustering, and regression. The R language is the statistical tool used to demonstrate the concepts throughout the book.

What will you learn? Write R programs to handle data. Build analytical models and draw useful inferences from them. Discover the basic concepts of data mining and machine learning. Carry out predictive modeling. Define a business issue as an analytical problem.

Who This Book Is For?Beginners who want to understand and learn the fundamentals of analytics using R. Students, managers, executives, strategy and planning professionals, software professionals, and BI/DW professionals.