Fundamentals of Machine Learning using Python

Cover
Title Page
Copyright
ABOUT THE AUTHORS
TABLE OF CONTENTS
List of Figures
List of Tables
List of Abbreviations
Preface
Chapter 1 Introduction to Python
	1.1. What Is Python
	1.2. What Makes Python Suitable For Machine Learning?
	1.3. What Are Other Computational Tools For Machine Learning?
	1.4. How To Obtain And Configure Python?
	1.5. Scientific Python Software Set
	1.6. Modules
	1.7. Notebooks
	1.8. Variables And Types
	1.9. Operators And Comparison
Chapter 2 Computing Things With Python
	2.1. Formatting And Printing to Screen
	2.2. Lists, Dictionaries, Tuples, And Sets
	2.3. Handling Files
	2.4. Exercises
	2.5. Python Statements
	2.6. For And While Loops
	2.7. Basic Python Operators
	2.8. Functions
Chapter 3 A General Outlook on Machine Learning
Chapter 4 Elements of Machine Learning
	4.1. What Is Machine Learning?
	4.2. Introduction To Supervised Learning
	4.3. Introduction To Unsupervised Learning
	4.4. A Challenging Problem: The Cocktail Party
Chapter 5 Linear Regression With One Variable
	5.1. Introduction
	5.2. Model Structure
	5.3. Cost Function
	5.4. Linear Regression Using Gradient Descent Method Using Python
	Exercises
Chapter 6 A General Review On Linear Algebra
	6.1. Introduction
	6.2. Matrices And Vectors
	6.3. Addition
	6.4. Multiplication
	6.5. Matrix-Vector Multiplication
	6.6. Matrix-Matrix Multiplication
	6.7. Inverse And Transpose
	Exercises
Chapter 7 Linear Regression With Multiple Inputs/Features
	7.1. Gradient Descent For Multiple Variables Linear Regression
	7.2. Normal Equation
	7.3. Programming Exercise: Linear Regression With Single Input And Multiple Inputs
Chapter 8 Classification Using Logistic Regression Model
	8.1. Logistic Regression Model Structure
	8.2. Concept Exercise: Classifying Success In Exam According Hours Of Study
	8.3. Programming Exercise: Implementation Of The Exam Results Problem In Python From Scratch
	8.4. Bonus: Logistic Regression Using Keras
Chapter 9 Regularization
	9.1. Regularized Linear Regression
Chapter 10 Introduction To Neural Networks
	10.1. The Essential Block: Neurons
	10.2. How To Implement A Neuron Using Python And Numpy
	10.3. Combining Neurons to Build a Neural Network
	10.4. Example Of Feedforward Neural Network
	10.5. How To Implement A Neural Network Using Python and Numpy
	10.6. How To Train A Neural Network
	10.7. Example Of Calculating Partial Derivatives
	10.8. Implementation Of A Complete Neural Network With Training Method
Chapter 11 Introduction To Decision Trees and Random Forest
	11.1. Decision Trees
	11.2. Random Forest
	11.3. Programming Exercise – Decision Tree From Scratch With Python
Chapter 12 Principal Component Analysis
	12.1. Introduction
	12.2. Mathematical Concepts
	12.3. Principal Component Analysis Using Python
Chapter 13 Classification Using K-Nearest Neighbor Algorithm
	13.1. Introduction
	13.2. Principles and Definition of KNN
	13.3. Algorithm
	13.4. Example and Python Solution
Chapter 14 Introduction To Kmeans Clustering
	14.1. How Kmeans Works?
	14.2. Kmeans Algorithm
Chapter 15 Computing With Tensorflow: Introduction And Basics
	15.1. Installing Tensorflow Library
	15.2. Tensors
	15.3. Computational Graph and Session
	15.4. Operating With Matrices
	15.5. Variables
	15.6. Placeholders
	15.7. Ways Of Creating Tensors
	15.8. Summary
Chapter 16 Tensorflow: Activation Functions And Optimization
	16.1. Activation Functions
	16.2. Loss Functions
	16.3. Optimizers
	16.4. Metrics
Chapter 17 Introduction To Natural Language Processing
	17.1. Definition Of Natural Language Processing
	17.2. Usage Of NLP
	17.3. Obstacles In NLP
	17.4. Techniques Used In NLP
	17.5. NLP Libraries
	17.6. Programming Exercise: Subject/Topic Extraction Using NLP
	17.7. Text Tokenize Using NLTK
	17.8. Synonyms From Wordnet
	17.9. Stemming Words With NLTK
	17.10. Lemmatization Using NLTK
Chapter 18 Project: Recognize Handwritten Digits Using Neural Networks
	18.1. Introduction
	18.2. Project Setup
	18.3. The Data
	18.4. The Algorithm
Appendix A
Bibliography
Index
Back Cover