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Machine Learning: Step-by-Step Guide To Implement Machine Learning Algorithms with Python by Rudolph Russell pdf download

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

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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