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 Contents

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