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Machine Learning Solutions

更新時間：2021-08-27 18:54:25

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Thisbookisfortheintermediateuserssuchasmachinelearningengineers,dataengineers,datascientists,andmore,whowanttosolvesimpletocomplexmachinelearningproblemsintheirday-to-dayworkandbuildpowerfulandefficientmachinelearningmodels.AbasicunderstandingofthemachinelearningconceptsandsomeexperiencewithPythonprogrammingisallyouneedtogetstartedwiththisbook.

目錄(148章)

倒序

封面
Machine Learning Solutions
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PacktPub.com
Foreword
Contributors
About the author
About the reviewer
Packt is Searching for Authors Like You
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Chapter 1. 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
Chapter 2. 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
Chapter 3. 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
Chapter 4. Recommendation Systems for E-Commerce
Introducing the problem statement
Understanding the datasets
Building the baseline approach
Building the revised approach
The best approach
Summary
Chapter 5. 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
Chapter 6. Job Recommendation Engine
Introducing the problem statement
Understanding the datasets
Building the baseline approach
Building the revised approach
The best approach
Summary
Chapter 7. 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
Chapter 8. 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
Chapter 9. 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
Chapter 10. 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
Chapter 11. 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
Appendix A. List of Cheat Sheets
Cheat sheets
Summary
Appendix B. Strategy for Wining Hackathons
Strategy for winning hackathons
Keeping up to date
Summary
Index 更新時間：2021-08-27 18:54:25

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Machine Learning Solutions