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. PDF
Preview 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.
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, duplicated or transmitted without direct written permission from the author or the publisher. Under no circumstances will any blame or legal responsibility be held against the publisher, or author, for any damages, reparation, or monetary loss due to the information contained within this book. Either directly or indirectly. Legal Notice: This book is copyright protected. This book is only for personal use. You cannot amend, distribute, sell, use, quote or paraphrase any part, or the content within this book, without the consent of the author or publisher. Disclaimer Notice: Please note the information contained within this document is for educational and entertainment purposes only. All effort has been executed to present accurate, up to date, and reliable, complete information. No warranties of any kind are declared or implied. Readers acknowledge that the author is not engaging in the rendering of legal, financial, medical or professional advice. The content within this book has been derived from various sources. Please consult a licensed professional before attempting any techniques outlined in this book. By reading this document, the reader agrees that under no circumstances is the author responsible for any losses, direct or indirect, which are incurred as a result of the use of information contained within this document, including, but not limited to, — errors, omissions, or inaccuracies. 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!
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