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Machine Learning Solutions
Jalaj Thanaki 著
更新時間：2021-08-27 18:54:25
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最新章節：
Index
Thisbookisfortheintermediateuserssuchasmachinelearningengineers,dataengineers,datascientists,andmore,whowanttosolvesimpletocomplexmachinelearningproblemsintheirday-to-dayworkandbuildpowerfulandefficientmachinelearningmodels.AbasicunderstandingofthemachinelearningconceptsandsomeexperiencewithPythonprogrammingisallyouneedtogetstartedwiththisbook.
最新章節
- Index
- Summary
- Keeping up to date
- Strategy for winning hackathons
- Appendix B. Strategy for Wining Hackathons
- Summary
品牌：中圖公司
上架時間：2021-08-27 18:06:11
出版社：Packt Publishing
本書數字版權由中圖公司提供，并由其授權上海閱文信息技術有限公司制作發行
- Index 更新時間：2021-08-27 18:54:25
- Summary
- Keeping up to date
- Strategy for winning hackathons
- Appendix B. Strategy for Wining Hackathons
- Summary
- Cheat sheets
- Appendix A. List of Cheat Sheets
- Summary
- Just for fun - implementing the Flappy Bird gaming bot
- Implementing the Pong gaming bot
- Building the Pong gaming bot
- Implementing the Space Invaders gaming bot
- Building the Space Invaders gaming bot
- Implementing the basic version of the gaming bot
- Basic Atari gaming bot
- Understanding Reinforcement Learning (RL)
- Setting up the coding environment
- Introducing the problem statement
- Chapter 11. Building Gaming Bot
- Summary
- The best approach
- How to optimize the existing approach
- Problems with the existing approach
- Testing the model
- Understanding the testing matrix
- Building the face emotion recognition model
- Understanding the concepts of face emotion recognition
- Understanding the dataset for face emotion recognition
- Approaches for implementing face recognition
- Understanding the concepts of face recognition
- Setting up the coding environment
- Introducing the problem statement
- Chapter 10. Face Recognition and Face Emotion Recognition
- Summary
- The best approach
- Implementing the revised approach
- How to optimize the existing approach
- Problem with existing approach
- Testing the baseline model
- Understanding the testing metrics
- Building the baseline model
- Selecting the machine learning algorithm
- Features engineering for the baseline model
- Setting up the coding environment
- Transfer Learning
- Understanding the dataset
- Introducing the problem statement
- Chapter 9. Building a Real-Time Object Recognition App
- Summary
- Discussing the hybrid approach
- The best approach
- Problems with the revised approach
- Testing the revised approach
- Implementing the revised approach
- Problems with the existing approach
- Testing the rule-based chatbot
- Implementing the rule-based chatbot
- Building the basic version of a chatbot
- Understanding datasets
- Introducing the problem statement
- Chapter 8. Developing Chatbots
- Summary
- The best approach
- Building the revised approach
- Building the baseline approach
- Understanding datasets
- Introducing the problem statement
- Understanding the basics of summarization
- Chapter 7. Text Summarization
- Summary
- The best approach
- Building the revised approach
- Building the baseline approach
- Understanding the datasets
- Introducing the problem statement
- Chapter 6. Job Recommendation Engine
- Summary
- The best approach
- Implementing the revised approach
- How to optimize the existing approach
- Problem with the existing approach
- Testing the baseline model
- Understanding the testing matrix
- Training the baseline model
- Selecting the machine learning algorithm
- Feature engineering for the baseline model
- Building the training and testing datasets for the baseline model
- Understanding the dataset
- Introducing problem statements
- Chapter 5. Sentiment Analysis
- Summary
- The best approach
- Building the revised approach
- Building the baseline approach
- Understanding the datasets
- Introducing the problem statement
- Chapter 4. Recommendation Systems for E-Commerce
- Summary
- Customer segmentation for various domains
- The best approach
- Building the revised approach
- Building the baseline approach
- Understanding the datasets
- Introducing customer segmentation
- Chapter 3. Customer Analytics
- Summary
- The best approach
- Implementing the revised approach
- Understanding the revised approach
- Exploring problems with the existing approach
- Testing the baseline model
- Understanding the testing matrix
- Training the baseline model
- Selecting the Machine Learning algorithm
- Feature engineering
- Data preprocessing and data analysis
- Understanding the dataset
- Collecting the dataset
- Introducing the problem statement
- Chapter 2. Stock Market Price Prediction
- Summary
- Best approach
- Implementing the revised approach
- Optimizing the existing approach
- Problems with the existing approach
- Testing the baseline model
- Understanding the testing matrix
- Training the baseline model
- Selecting machine learning algorithms
- Feature engineering for the baseline model
- Understanding the dataset
- Introducing the problem statement
- Chapter 1. Credit Risk Modeling
- Get in touch
- To get the most out of this book
- What this book covers
- Who this book is for
- Preface
- Packt is Searching for Authors Like You
- About the reviewer
- About the author
- Contributors
- Foreword
- PacktPub.com
- Why subscribe?
- Machine Learning Solutions
- 封面
- 封面
- Machine Learning Solutions
- Why subscribe?
- 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
