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

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

This book is for anyone World Health Organization would love to find out a way to develop machine-learning systems. we are going to cowl the foremost necessary ideas regarding machine learning algorithms, in each a theoretical and a sensible approach, and we’ll implement several machine-learning algorithms mistreatment the Scikit-learn library within the Python programing language. within the initial chapter, you may learn the foremost necessary ideas of machine learning, and, within the next chapter, you may work primarily with the classification. within the last chapter you may find out how to coach your model. I assume that you’ve got data of the fundamentals of programming

This book contains illustrations and bit-by-bit explanations with bullet points and exercises for simple and pleasant learning.

Benefits of reading this book that you are not progressing to realize anyplace else:

Introduction to Machine Learning

Classification

How to train a Model

Different Models combos

Table of Contents:

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

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

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