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

Introducing the problem statement

As you know, in this chapter, we are trying to build a recommendation system. A domain that mainly uses the recommendation system is e-commerce. So, in our basic version of the recommendation engine specifically, we will be building an algorithm that can suggest the name of the products based on the category of the product. Once we know the basic concepts of the recommendation engine, we will build a recommendation engine that can suggest books in the same way as the Amazon website.

We will be building three versions of the recommendation algorithm. The baseline approach is simple but intuitive so that readers can learn what exactly the recommendation algorithm is capable of doing. Baseline is easy to implement. In the second and third approach, we will be building the book recommendation engine using ML algorithms.

Let's look at the basic methods or approaches that are used to build the recommendation system. There are two main approaches, which you can find...

Machine Learning Solutions

Machine Learning Solutions

Overview of this book

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Introducing the problem statement