目錄(81章)
倒序
- 封面
- 版權信息
- Credits
- Foreword
- About the Author
- Acknowledgments
- About the Reviewers
- www.PacktPub.com
- Preface
- Chapter 1. Introduction to Machine Learning
- Objective
- Why use F#?
- Unsupervised learning
- Machine learning frameworks
- Machine learning for fun and profit
- Recognizing handwritten digits – your "Hello World" ML program
- Summary
- Chapter 2. Linear Regression
- Objective
- Different types of linear regression algorithms
- APIs used
- The basics of matrices and vectors (a short and sweet refresher)
- QR decomposition of a matrix
- Linear regression method of least square
- Finding linear regression coefficients using F#
- Finding the linear regression coefficients using Math.NET
- Putting it together with Math.NET and FsPlot
- Multiple linear regression
- Multiple linear regression and variations using Math.NET
- Weighted linear regression
- Plotting the result of multiple linear regression
- Ridge regression
- Multivariate multiple linear regression
- Feature scaling
- Summary
- Chapter 3. Classification Techniques
- Objective
- Different classification algorithms you will learn
- Some interesting things you can do
- Understanding logistic regression
- Multiclass classification using logistic regression
- Multiclass classification using decision trees
- Predicting a traffic jam using a decision tree: a case study
- Challenge yourself!
- Summary
- Chapter 4. Information Retrieval
- Objective
- Different IR algorithms you will learn
- What interesting things can you do?
- Information retrieval using tf-idf
- Chapter 5. Collaborative Filtering
- Objective
- Different classification algorithms you will learn
- Vocabulary of collaborative filtering
- Baseline predictors
- Item-item collaborative filtering
- Top-N recommendations
- Evaluating recommendations
- Ranking accuracy metrics
- Working with real movie review data (Movie Lens)
- Summary
- Chapter 6. Sentiment Analysis
- Objective
- What you will learn
- A baseline algorithm for SA using SentiWordNet lexicons
- Handling negations
- Identifying praise or criticism with sentiment orientation
- Pointwise Mutual Information
- Using SO-PMI to find sentiment analysis
- Summary
- Chapter 7. Anomaly Detection
- Objective
- Detecting point anomalies using IQR (Interquartile Range)
- Detecting point anomalies using Grubb's test
- Grubb's test for multivariate data using Mahalanobis distance
- Chi-squared statistic to determine anomalies
- Detecting anomalies using density estimation
- Strategy to convert a collective anomaly to a point anomaly problem
- Dealing with categorical data in collective anomalies
- Summary
- Index 更新時間：2021-07-16 13:07:19
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