Python Machine Learning. A Crash Course for Beginners to Understand Machine learning, Artificial Intelligence, Neural Networks, and Deep Learning with Scikit-Learn, TensorFlow, and Keras.

178 Pages·2019·1.3 MB·English

by  Josh Hugh Learning

Python Machine Learning

A Crash Course for Beginners to
Understand Machine learning, Artificial
Intelligence, Neural Networks, and Deep
Learning with Scikit-Learn, TensorFlow,
and Keras.

by
Josh Hugh Learning
Josh Hugh Learning

© Copyright 2019 - All rights reserved.

The content contained within this book may not be reproduced,
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Please consult a licensed professional before attempting any
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Table of Contents

Introduction
Chapter 1: The Basics of Machine Learning
The Benefits of Machine Learning
Supervised Machine Learning
Unsupervised Machine Learning
Reinforcement Machine Learning
Chapter 2: Learning the Data sets of Python
Structured Data Sets
Unstructured Data Sets
How to Manage the Missing Data
Splitting Your Data
Training and Testing Your Data
Chapter 3: Supervised Learning with Regressions
The Linear Regression
The Cost Function
Using Weight Training with Gradient Descent
Polynomial Regression
Chapter 4: Regularization
Different Types of Fitting with Predicted Prices
How to Detect Overfitting
How Can I Fix Overfitting?
Chapter 5: Supervised Learning with Classification
Logistic Regression
Multiclass Classification
Chapter 6: Non-linear Classification Models
K-Nearest Neighbor
Decision Trees and Random Forests
Working with Support Vector Machines
The Neural Networks
Chapter 7: Validation and Optimization Techniques
Cross-Validation Techniques
Hyperparameter Optimization
Grid and Random Search
Chapter 8: Unsupervised Machine Learning with Clustering
K-Means Clustering
Hierarchal Clustering
DBSCAN
Chapter 9: Reduction of Dimensionality
The Principal Component Analysis
Linear Discriminant Analysis
Comparing PCA and LDA
Conclusion
Introduction

Congratulations on purchasing Python Machine Learning, and
thank you for doing so.

The following chapters will discuss a lot of the different parts that we
need to know when it is time to start working with the Python
language and getting it to work for some of your own machine
learning needs. There are many companies that want to work with
machine learning in order to help them learn more about their
company, their competition, their industry, and their customers.
When we collect the right data and combine it with the right machine
learning algorithms, we will be able to make this work for our needs
Sometimes, getting started with machine learning is hard, and
knowing how to get your own program set up and ready to go will be
important. The hardest part is figuring out the algorithms that we are
going to spend some time working on along the way. There are really
quite a few machine learning algorithms that you are able to work
with, and picking the right one often will depend on the different
processes that you want to do, the questions that you want the data
to answer for you, and even the kind of data that you are trying to
work with.

We are going to look at some of the basics that come with the
process of machine learning and how to pick out the kind of data that
we are able to work with as well. Then we will spend the rest of this
guidebook looking at some of the different algorithms that we want to
handle in this kind of language, with the help of Python. These will
ensure that we are able to take over make sure that our data is
handled and that we are actually able to see results with the work
that we need to do.
There are many types of algorithms that we are able to explore.
Some of the options that we are going to explore in this guidebook
will include regressions, linear classification, non-linear, and more. In
each of these categories, we are going to spend our time looking at
how we can get started with this process, and the types of algorithms
that fit into each one, and more. When you are done with this
guidebook, you will know what you need about some of the most
common machine learning algorithms and how to use them for your
own data analysis.

There is so much that we are able to do with the Python language,
and learning how to use it to pick out the right machine learning can
be important. When you are ready to get started with Python
machine learning, make sure to check out this guidebook to help you
get started.

There are plenty of books on this subject on the market, thanks
again for choosing this one! Every effort was made to ensure it is full
of as much useful information as possible, and please enjoy it!