Machine Learning: Step-by-Step Guide To Implement Machine Learning Algorithms with Python

Questions
1. Explain how to combine different models in detail.
2. What are the goals and benefits of combining models?

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Document Outline

• CHAPTER 1
• INTRODUCTION TO MACHINE LEARNING
  • Theory
  • What is machine learning?
  • Why machine learning?
  • When should you use machine learning?
  • Types of Systems of Machine Learning
  • Supervised and unsupervised learning
    • Supervised Learning
    • The most important supervised algorithms
    • Unsupervised Learning
    • The most important unsupervised algorithms
  • Reinforcement Learning
  • Batch Learning
  • Online Learning
  • Instance based learning
  • Model-based learning
  • Bad and Insufficient Quantity of Training Data
  • Poor-Quality Data
  • Irrelevant Features
    • Feature Engineering
  • Testing
  • Overfitting the Data
    • Solutions
  • Underfitting the Data
    • Solutions
  • EXERCISES
  • SUMMARY
  • REFERENCES
• CHAPTER 2
• CLASSIFICATION
  • Installation
  • The MNIST
  • Measures of Performance
  • Confusion Matrix
  • Recall
  • Recall Tradeoff
  • ROC
  • Multi-class Classification
  • Training a Random Forest Classifier
  • Error Analysis
  • Multi-label Classifications
  • Multi-output Classification
  • EXERCISES
  • REFERENCES
• CHAPTER 3
• HOW TO TRAIN A MODEL
  • Linear Regression
  • Computational Complexity
  • Gradient Descent
  • Batch Gradient Descent
  • Stochastic Gradient Descent
  • Mini-Batch Gradient Descent
  • Polynomial Regression
  • Learning Curves
  • Regularized Linear Models
    • Ridge Regression
    • Lasso Regression
  • EXERCISES
  • SUMMARY
  • REFERENCES
• Chapter 4
• Different models combinations
  • Implementing a simple majority classifer
  • Combining different algorithms for classification with majority vote
  • Questions