Book Image

Machine Learning Solutions

Book Image

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.

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Table of Contents (19 chapters)
Machine Learning Solutions
Machine Learning Solutions
Foreword
Foreword
Contributors
Contributors
Preface
Preface
Free Chapter
1
Credit Risk Modeling
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
2
Stock Market Price Prediction
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
3
Customer Analytics
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
4
Recommendation Systems for E-Commerce
Recommendation Systems for E-Commerce
Introducing the problem statement
Understanding the datasets
Building the baseline approach
Building the revised approach
The best approach
Summary
5
Sentiment Analysis
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
6
Job Recommendation Engine
Job Recommendation Engine
Introducing the problem statement
Understanding the datasets
Building the baseline approach
Building the revised approach
The best approach
Summary
7
Text Summarization
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
8
Developing Chatbots
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
9
Building a Real-Time Object Recognition App
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
10
Face Recognition and Face Emotion Recognition
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
11
Building Gaming Bot
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
List of Cheat Sheets
Cheat sheets
Summary
Strategy for Wining Hackathons
Strategy for Wining Hackathons
Strategy for winning hackathons
Keeping up to date
Summary
Index
Index

Contributors

About the author

Jalaj is an experienced data scientist with a demonstrated history of working in the information technology, publishing, and finance industries. She is author of the book "Python Natural Language Processing".

Her research interest lies in Natural Language Processing, Machine Learning, Deep Learning, and Big Data Analytics. Besides being a data scientist, Jalaj is also a social activist, traveler, and nature-lover.

I would like to dedicate this book to my husband, Shetul, for his constant support and encouragement. I give deep thanks and gratitude to my parents and my in-laws who help me at every stage of my life. I would like to thank my reviewers for providing valuable suggestions towards the improvement of this book.

Thank you, God, for being kind to me...!

About the reviewer

Niclas has been using computers for fun and profit since he got his first computer (a C64) at age four. After a prolonged period of combining a start-up with university studies, he graduated from Åbo Akademi University with an M.Sc. in Computer Engineering in 2015.

His hobbies include long walks, lifting heavy metal objects at the gym and spending quality time with his wife and daughter.

Niclas currently works at Walkbase - a company he founded together with some of his class mates from university. At Walkbase, he leads the engineering team, building the next generation of retail analytics.

Mayur Narkhede has good blend of experience in Data Science and Industry domain. He is researcher with educational qualification of B.Tech in Computer Science and M.Tech. in CSE with Artificial Intelligence specialization.

Data Scientist with core experience in building automated end to end solutions, Proficient at applying technology, AI, ML, Data Mining and Design thinking for better understanding and prediction in improving business functions and desired requirements with growth profitability.

He has worked on multiple advanced solutions such as ML and Predictive model development for Oil and Gas, Financial Services, Road Traffic and Transport, Life Science and Big Data platform for asset intensive industries.

Shetul Thanaki has a bachelor degree in computer engineering from Sardar vallabhbhai patel University,Gujarat. He has 10 years of experience in IT industries. He is currently working with investment bank and provides IT solutions to its global market applications.

He has good knowledge on java technologies,rule based system and database systems. AI technologies in fintech space are one of his key interest areas.

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