Of all the supervised machine learning techniques, the linear regression algorithm is the easiest one to understand. We will first look at simple linear regression and then we will expand the concept to multiple linear regression.
- Title Page
- Copyright and Credits
- Dedication
- About Packt
- Contributors
- Preface
- Section 1: Fundamentals and Core Algorithms
- Overview of Algorithms
- Data Structures Used in Algorithms
- Sorting and Searching Algorithms
- Designing Algorithms
- Graph Algorithms
- Section 2: Machine Learning Algorithms
- Unsupervised Machine Learning Algorithms
- Introducing unsupervised learning
- Understanding clustering algorithms
- Dimensionality reduction
- Association rules mining
- Practical application– clustering similar tweets together
- Anomaly-detection algorithms
- Summary
- Traditional Supervised Learning Algorithms
- Understanding supervised machine learning
- Understanding classification algorithms
- Presenting the classifiers challenge
- Evaluating the classifiers
- Specifying the phases of classifiers
- Decision tree classification algorithm
- Understanding the ensemble methods
- Logistic regression
- The SVM algorithm
- Understanding the naive Bayes algorithm
- For classification algorithms, the winner is...
- Understanding regression algorithms
- Practical example – how to predict the weather
- Summary
- Neural Network Algorithms
- Algorithms for Natural Language Processing
- Recommendation Engines
- Introducing recommendation systems
- Types of recommendation engines
- Understanding the limitations of recommender systems
- Areas of practical applications
- Practical example – creating a recommendation engine
- Summary
- Section 3: Advanced Topics
- Data Algorithms
- Cryptography
- Introduction to Cryptography
- Understanding the Types of Cryptographic Techniques
- Example – Security Concerns When Deploying a Machine Learning Model
- Summary
- Large-Scale Algorithms
- Practical Considerations
- Other Books You May Enjoy
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