Machine Learning Solutions

Machine Learning Solutions

Overview of this book

Machine learning (ML) helps you find hidden insights from your data without the need for explicit programming. This book is your key to solving any kind of ML problem you might come across in your job. You’ll encounter a set of simple to complex problems while building ML models, and you'll not only resolve these problems, but you’ll also learn how to build projects based on each problem, with a practical approach and easy-to-follow examples. The book includes a wide range of applications: from analytics and NLP, to computer vision domains. Some of the applications you will be working on include stock price prediction, a recommendation engine, building a chat-bot, a facial expression recognition system, and many more. The problem examples we cover include identifying the right algorithm for your dataset and use cases, creating and labeling datasets, getting enough clean data to carry out processing, identifying outliers, overftting datasets, hyperparameter tuning, and more. Here, you'll also learn to make more timely and accurate predictions. In addition, you'll deal with more advanced use cases, such as building a gaming bot, building an extractive summarization tool for medical documents, and you'll also tackle the problems faced while building an ML model. By the end of this book, you'll be able to fine-tune your models as per your needs to deliver maximum productivity.

Machine Learning Solutions

Foreword

Contributors

Preface

Free Chapter

Credit Risk Modeling

Introducing the problem statement

Understanding the dataset

Feature engineering for the baseline model

Selecting machine learning algorithms

Training the baseline model

Understanding the testing matrix

Testing the baseline model

Problems with the existing approach

Optimizing the existing approach

Implementing the revised approach

Best approach

Summary

Stock Market Price Prediction

Introducing the problem statement

Collecting the dataset

Understanding the dataset

Data preprocessing and data analysis

Feature engineering

Selecting the Machine Learning algorithm

Training the baseline model

Understanding the testing matrix

Testing the baseline model

Exploring problems with the existing approach

Understanding the revised approach

Implementing the revised approach

The best approach

Summary

Customer Analytics

Introducing customer segmentation

Understanding the datasets

Building the baseline approach

Building the revised approach

The best approach

Customer segmentation for various domains

Summary

Recommendation Systems for E-Commerce

Introducing the problem statement

Understanding the datasets

Building the baseline approach

Building the revised approach

The best approach

Summary

Sentiment Analysis

Introducing problem statements

Understanding the dataset

Building the training and testing datasets for the baseline model

Feature engineering for the baseline model

Selecting the machine learning algorithm

Training the baseline model

Understanding the testing matrix

Testing the baseline model

Problem with the existing approach

How to optimize the existing approach

Implementing the revised approach

The best approach

Summary

Job Recommendation Engine

Introducing the problem statement

Understanding the datasets

Building the baseline approach

Building the revised approach

The best approach

Summary

Text Summarization

Understanding the basics of summarization

Introducing the problem statement

Understanding datasets

Building the baseline approach

Building the revised approach

The best approach

Summary

Developing Chatbots

Introducing the problem statement

Understanding datasets

Building the basic version of a chatbot

Implementing the rule-based chatbot

Testing the rule-based chatbot

Problems with the existing approach

Implementing the revised approach

Testing the revised approach

Problems with the revised approach

The best approach

Discussing the hybrid approach

Summary

Building a Real-Time Object Recognition App

Introducing the problem statement

Understanding the dataset

Transfer Learning

Setting up the coding environment

Features engineering for the baseline model

Selecting the machine learning algorithm

Building the baseline model

Understanding the testing metrics

Testing the baseline model

Problem with existing approach

How to optimize the existing approach

Implementing the revised approach

The best approach

Summary

Face Recognition and Face Emotion Recognition

Introducing the problem statement

Setting up the coding environment

Understanding the concepts of face recognition

Approaches for implementing face recognition

Understanding the dataset for face emotion recognition

Understanding the concepts of face emotion recognition

Building the face emotion recognition model

Understanding the testing matrix

Testing the model

Problems with the existing approach

How to optimize the existing approach

The best approach

Summary

Building Gaming Bot

Introducing the problem statement

Setting up the coding environment

Understanding Reinforcement Learning (RL)

Basic Atari gaming bot

Implementing the basic version of the gaming bot

Building the Space Invaders gaming bot

Implementing the Space Invaders gaming bot

Building the Pong gaming bot

Implementing the Pong gaming bot

Just for fun - implementing the Flappy Bird gaming bot

Summary

List of Cheat Sheets

Cheat sheets

Summary

Strategy for Wining Hackathons

Strategy for winning hackathons

Keeping up to date

Summary

Index

Customer Reviews

5 star

4 star

3 star

2 star

1 star

Foreword

I have known Jalaj Thanaki for more than 1 year. Jalaj comes across as a passionate techno-analytical expert who has the rigor one requires to achieve excellence. Her points of view on big data analytics, NLP, machine learning, and AI are well informed and carry her own analysis and appreciation of the landscape of problems and solutions. I'm glad to be writing this foreword in my capacity as the CEO and MD of SMEcorner.

Machine Learning solutions are rapidly changing the world and the way we do business, be it retail, banking, financial services, publication, pharmaceutical, or manufacturing industry. Data of all forms is growing exponentially—quantitative, qualitative, structured, unstructured, speech, video, and so on. It is imperative to make use of this data to leverage all functions, avoid risk and frauds, enhance customer experience, increase revenues, and streamline operations.

Organizations are moving fast to embrace data science and investing largely to build high-end data science teams. Having spent more than 30 years in the finance domain as a leader and managing director of various organizations such as Barclays Bank, Equifax, Hinduja Leyland, and SMECorner, I get overwhelmed with the transition that the financial industry has seen in embracing machine learning solutions as a business and no longer as a support function.

In this book, Jalaj takes us through an exciting and insightful journey to develop the best possible machine learning solutions for data science applications. With all the practical examples covered and with solid explanations, in my opinion, this is one of the best practical books for readers who want to become proficient in machine learning and deep learning.

Wishing Jalaj and this book a roaring success, which they deserve.

Samir Bhatia

MD/CEO and Founder of SMECorner

Mumbai, India

Machine Learning Solutions

Machine Learning Solutions

Overview of this book

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Foreword