Thanaki J. Machine Learning Solutions

Packt Publishing, 2018. — 566 p. — ISBN: 1788390040.Practical, hands-on solutions in Python to overcome any problem in Machine LearningMachine 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.Who this book is forWhat this book coversTo get the most out of this book
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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 approachStock 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 approachCustomer Analytics
Introducing customer segmentation
Understanding the datasets
Building the baseline approach
Building the revised approach
The best approach
Customer segmentation for various domainsRecommendation Systems for E-Commerce
Introducing the problem statement
Understanding the datasets
Building the baseline approach
Building the revised approach
The best approachSentiment 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 approachJob Recommendation Engine
Introducing the problem statement
Understanding the datasets
Building the baseline approach
Building the revised approach
The best approachText Summarization
Understanding the basics of summarization
Introducing the problem statement
Understanding datasets
Building the baseline approach
Building the revised approach
The best approachDeveloping 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 approachBuilding 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 approachFace Recognition and Face Emotion Recognition
Introducing the problem statement
Setting up the coding environment
Understanding the face recognition dataset
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 approachBuilding 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 botA. List of Cheat Sheets
B. Strategy for Wining Hackathons