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 in-depth 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 real-life 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 real-world problems
- Perform predictive analysis using decision trees and random forests
- Carry out clustering using the k-means 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 school-level mathematics and at least one programming language (preferably Python).
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: Pre-Processing Data
- Minmax Scaling of the Goal Column
- Identifying Features and Labels
- Cross-Validation with scikit-learn
- Activity 7: Preparing Credit Data for Classification
- The k-nearest neighbor Classifier
- Introducing the K-Nearest Neighbor Algorithm
- Distance Functions
- Exercise 12: Illustrating the K-nearest Neighbor Classifier Algorithm
- Exercise 13: k-nearest Neighbor Classification in scikit-learn
- Exercise 14: Prediction with the k-nearest neighbors classifier
- Parameterization of the k-nearest neighbor Classifier in scikit-learn
- 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 scikit-learn
- 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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