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

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

Assuming that I get some information about “Machine learning,” you’ll likely envision a robot or something like the Terminator. Truly t, machine learning is involved not just in advanced mechanics, yet additionally in numerous different applications. You can likewise envision something like a spam channel as being one of the principal applications in ML, which works on the existence of millions of individuals. In this part, I’ll present to you what machine learning is, and the way that it works.

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Table of Content

CHAPTER 1 INTRODUCTION TO MACHINE LEARNING

  • 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 classifier
  • Combining different algorithms for classification with the majority vote
  • Questions

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