Book description
Create AI applications in Python and lay the foundations for your career in data science
Key Features
 Practical examples that explain key machine learning algorithms
 Explore neural networks in detail with interesting examples
 Master core AI concepts with engaging activities
Book Description
Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover indepth mathematical topics, such as regression and classification, illustrated by Python examples.
As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on reallife datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore's law.
By the end of this book, you will be confident when it comes to building your own AI applications with your newly acquired skills!
What you will learn
 Understand the importance, principles, and fields of AI
 Implement basic artificial intelligence concepts with Python
 Apply regression and classification concepts to realworld problems
 Perform predictive analysis using decision trees and random forests
 Carry out clustering using the kmeans and mean shift algorithms
 Understand the fundamentals of deep learning via practical examples
Who this book is for
Artificial Intelligence and Machine Learning Fundamentals is for software developers and data scientists who want to enrich their projects with machine learning. You do not need any prior experience in AI. However, it's recommended that you have knowledge of high schoollevel mathematics and at least one programming language (preferably Python).
Publisher resources
Table of contents
 Preface
 Principles of Artificial Intelligence
 AI with Search Techniques and Games

Regression
 Introduction
 Linear Regression with One Variable

Linear Regression with Multiple Variables
 Multiple Linear Regression
 The Process of Linear Regression
 Importing Data from Data Sources
 Loading Stock Prices with Yahoo Finance
 Loading Files with pandas
 Loading Stock Prices with Quandl
 Exercise 8: Using Quandl to Load Stock Prices
 Preparing Data for Prediction
 Performing and Validating Linear Regression
 Predicting the Future

Polynomial and Support Vector Regression
 Polynomial Regression with One Variable
 Exercise 9: 1st, 2nd, and 3rd Degree Polynomial Regression
 Polynomial Regression with Multiple Variables
 Support Vector Regression
 Support Vector Machines with a 3 Degree Polynomial Kernel
 Activity 6: Stock Price Prediction with Quadratic and Cubic Linear Polynomial Regression with Multiple Variables
 Summary

Classification
 Introduction

The Fundamentals of Classification
 Exercise 10: Loading Datasets
 Data Preprocessing
 Exercise 11: PreProcessing Data
 Minmax Scaling of the Goal Column
 Identifying Features and Labels
 CrossValidation with scikitlearn
 Activity 7: Preparing Credit Data for Classification
 The knearest neighbor Classifier
 Introducing the KNearest Neighbor Algorithm
 Distance Functions
 Exercise 12: Illustrating the Knearest Neighbor Classifier Algorithm
 Exercise 13: knearest Neighbor Classification in scikitlearn
 Exercise 14: Prediction with the knearest neighbors classifier
 Parameterization of the knearest neighbor Classifier in scikitlearn
 Activity 8: Increasing the Accuracy of Credit Scoring
 Classification with Support Vector Machines
 Summary

Using Trees for Predictive Analysis

Introduction to Decision Trees
 Entropy
 Exercise 15: Calculating the Entropy
 Information Gain
 Gini Impurity
 Exit Condition
 Building Decision Tree Classifiers using scikitlearn
 Evaluating the Performance of Classifiers
 Exercise 16: Precision and Recall
 Exercise 17: Calculating the F1 Score
 Confusion Matrix
 Exercise 18: Confusion Matrix
 Activity 10: Car Data Classification
 Random Forest Classifier
 Summary

Introduction to Decision Trees
 Clustering

Deep Learning with Neural Networks
 Introduction
 TensorFlow for Python

Introduction to Neural Networks
 Biases
 Use Cases for Artificial Neural Networks
 Activation Functions
 Exercise 23: Activation Functions
 Forward and Backward Propagation
 Configuring a Neural Network
 Importing the TensorFlow Digit Dataset
 Modeling Features and Labels
 TensorFlow Modeling for Multiple Labels
 Optimizing the Variables
 Training the TensorFlow Model
 Using the Model for Prediction
 Testing the Model
 Randomizing the Sample Size
 Activity 14: Written Digit Detection
 Deep Learning
 Summary

Appendix
 Chapter 1: Principles of AI

Chapter 2: AI with Search Techniques and Games
 Activity 2: Teach the agent realize situations when it defends against losses
 Activity 3: Fix the first and second moves of the AI to make it invincible
 Activity 4: Connect Four
 Chapter 3: Regression
 Activity 5: Predicting Population
 Activity 6: Stock Price Prediction with Quadratic and Cubic Linear Polynomial Regression with Multiple Variables
 Chapter 4: Classification
 Chapter 5: Using Trees for Predictive Analysis
 Chapter 6: Clustering
 Chapter 7: Deep Learning with Neural Networks
Product information
 Title: Artificial Intelligence and Machine Learning Fundamentals
 Author(s):
 Release date: December 2018
 Publisher(s): Packt Publishing
 ISBN: 9781789801651
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