Machine Learning - A Complete Exploration of Highly Advanced Machine Learning Concepts, Best Practices and Techniques: 4

By Peter Bradley

Do you know how to build a Machine Learning Algorithm in Python?

Have you learned how to build a Neural Network in Python?

If you have read the first three books in the series, you will know how to do both those things. If you want to learn more about the concepts related to Machine Learning, and some subjects and concepts that are linked to Machine Learning, you have come to the right place.

Over the course of the book, you will gather information on the following:

Subjects linked to Machine Learning

Artificial Intelligence

Big Data

Building Generic Algorithms in Python

Activation functions used to build Neural Networks

Building a Neural Network in R

The information in this book will help you learn more about Machine Learning. You should now be able to link some of the concepts in Machine Learning with the work you do, or the work you want to do. Once you practice the models in the book, you can build your very own models in either R or Python.

So What are You Waiting For? It is never to early or late to learn. Grab a copy of this book Now, and build your very own genetic Algorithm in Python and a Neural Network in R.

• Language: English
• Publisher: Peter Bradley
• Release date: Jun 25, 2019
• ISBN: 9781393231196

Peter Bradley

Peter Bradley was the Labour MP for The Wrekin between 1997 and 2005. More recently, he co-founded and directed Speakers’ Corner Trust, a charity which promotes freedom of expression, open debate and active citizenship in the UK and developing democracies. He has written, usually on politics, for a wide range of publications, including The Times, The Guardian, The Independent, The New Statesman and The New European.

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Please note the information contained within this document is for educational and entertainment purposes only. Every attempt has been made to provide accurate, up to date and complete reliable information. No warranties of any kind are expressed or implied. Readers acknowledge that the author is not engaging in the rendering of legal, financial, medical or professional advice. The content of this book has been derived from various sources. Please consult a licensed professional before attempting any techniques outlined in this book.

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Table of Contents

Introduction

Chapter One: An introduction to Descriptive Statistics

Types of Data

Numerical

Categorical

Dichotomous Data

Nominal Data

Ordinal Data

Frequency Distributions

Histogram and Grouped Frequency Distributions

Measures of Location

Mean

Median

Mode

Symmetry and Skewness

Probability

Fundamental Axioms

Additive Property

Joint Probability

Conditional Probability

Bayes’ Rule

Understanding Random Variables and Expectations

Chapter Two: An introduction to Artificial Intelligence

Increased Computational Resources

Growth of Data

Deeper Focus

Knowledge Engineering

Alternative Reasoning Models

Exploring AI

Strong AI

Weak AI

Anything in Between

Chapter Three: The Artificial Intelligence Ecosystem

Understanding that AI is everywhere

What makes human beings so smart?

Sensing

Reasoning

Acting

Examining the Components of AI

Sensing

Reasoning

Acting

Assessing Data using AI

Predicting Outcomes with AI

Chapter Four: Big Data and Artificial Intelligence

What is Big Data?

Volume

Velocity

Variety

Veracity

Chapter Five: Building a Genetic Algorithm in Python

Chapter Six: Activation Functions Used to Develop Deep Learning Models

Popular Activation Functions

Binary Step Function

Sigmoid Function

Tanh

ReLU

Choosing the Right Activation Function

Chapter Seven: Building a Neural Network in R

Create Training Data

Create an object to store the state of our neural network

Activation Function

Loss Function

Train the Model

Chapter Eight: Fitting a Neural Network

Preparing to fit the neural network

Parameters

Predicting medv using the neural network

A (fast) cross validation

A final note on model interpretability

Conclusion

Sources

Introduction

In the last three parts of this series, we covered the basics of Machine Learning and the different subjects and algorithms that one can use to build a Machine Learning model. You also learned how to build a Machine Learning model in Python using the clustering and regression models.

Over the course of this book, you will gather information on some statistical concepts that one uses in Machine Learning. You will also learn about the different fields that are linked to Machine Learning. It is important to learn how these different concepts are intertwined, so you can build better models. You will also learn how you can build a genetic algorithm in Python and how to build a Neural Network in R.

Thank you for purchasing the book, ‘Machine Learning - A Complete Exploration of Highly Advanced Machine Learning Concepts, Best Practices and Techniques’ and I hope you find the book as useful as you considered the previous books in the series to be.

I hope you gather all the information you are looking for.

Chapter One: An introduction to Descriptive Statistics

This chapter deals with descriptive statistics, that is, the methodology for describing or summarizing a set of data using tables, diagrams and numerical measures. 

Presenting the data in a descriptive form is usually the first stage in any statistical analysis, as it allows us to spot any patterns in the data.  The numerical measures mentioned are the ‘average’ of the data (i.e., mean, median, and mode) and the ‘spread’ of the data (i.e., range, IQR, and variance). 

Types of Data

Batch data are a set of related observations, such as the current inflation rates of EU countries. Sample data are a set of observations selected from a population and designed to be representative of that population, such as the sums assured for a sample of 100 policies