Learn AI with Python
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Description
Contents
Reviews
Language
English
ISBN
9789391392611
Cover Page
Title Page
Copyright Page
Dedication Page
About the Author
About the Reviewer
Acknowledgement
Preface
Errata
Table of Contents
1. Introduction to AI and Python
Introduction
Structure
Objectives
Introduction to Artificial Intelligence (AI)
Why to learn AI?
Understanding intelligence
Types of intelligence
Various fields of study in AI
Applications of AI in various industries
How does artificial intelligence learn?
AI agents and environments
What is an agent?
What is an agent’s environment?
AI and Python – how do they relate?
What is Python?
Why choose Python for building AI applications?
Python3 – installation and setup
Windows
Linux
Ubuntu
Linux Mint
CentOS
Fedora
Installing and compiling Python from Source
macOS/Mac OS X
Conclusion
Questions
2. Machine Learning and Its Algorithms
Introduction
Structure
Objectives
Understanding Machine Learning (ML)
The Landscape of Machine Learning Algorithms
Components of a Machine Learning algorithm
Different learning styles in machine learning algorithms
Supervised learning
Unsupervised learning
Semi-supervised learning
Reinforcement learning
Popular machine learning algorithms
Linear regression
Logistic regression
Decision tree algorithm
Random forest
Naïve Bayes algorithm
Support Vector Machine (SVM)
k-Nearest Neighbor (kNN)
K-Means clustering
Conclusion
Questions
3. Classification and Regression Using Supervised Learning
Introduction
Structure
Objectives
Classification
Various steps to build a classifier using Python
Step 1 – Import ML library
Step 2 – Import dataset
Step 3 – Organizing data-training and testing set
Step 4 – Creating ML model
Step 5 – Train the model
Step 6 – Predicting test set result
Step 7 – Evaluating the accuracy
Lazy earning versus eager learning
Performance metrics for classification
Confusion matrix
Accuracy
Precision
Recall
Specificity
F1 score
Regression
Various steps to build a regressor using Python
Step 1 – Import ML library
Step 2 – Import dataset
Step 3 – Organizing data into training and testing set
Step 4 – Creating ML model
Step 5 – Train the model
Step 6 – Plotting the regression line
Step 7 – Calculating the variance
Performance metrics for regression
Mean Absolute Error (MAE)
Mean Squared Error (MSE)
R-Squared (R2)
Adjusted R-squared (R2)
Conclusion
Questions
4. Clustering Using Unsupervised Learning
Introduction
Structure
Objectives
Clustering
Various methods to form clusters
Important ML clustering algorithms
K-means clustering algorithm
Mean-shift clustering algorithm
Hierarchical clustering algorithm
Performance metrics for clustering
Silhouette analysis
Davies–Bouldin index
Dunn index
Conclusion
Questions
5. Solving Problems with Logic Programming
Introduction
Structure
Objectives
Logic programming
Building blocks of logic programming
Useful Python packages for logic programming
Implementation examples
Checking and generating prime numbers
Solving the puzzles
Conclusion
Questions
6. Natural Language Processing with Python
Introduction
Structure
Objective
Natural Language Processing (NLP)
Working of NLP
Phases/logical steps in NLP
Implementing NLP
Installing Python’s NLTK Package
Installing NLTK
Downloading NLTK corpus
Understanding tokenization, stemming, and lemmatization
Tokenization
Stemming
Lemmatization
Difference between lemmatization and stemming
Understanding chunking
Importance of chunking
Understanding Bag-of-Words (BoW) model
Why the BoW algorithm?
Implementing the BoW algorithm using Python
Understanding stop words
When to remove stop words?
Removing stop words using the NLTK library
Understanding vectorization and transformers
Vectorization techniques
Transformers
Some examples
Predicting the category
Gender finding
Conclusion
Questions
7. Implementing Speech Recognition with Python
Introduction
Structure
Objective
Basics of speech recognition
Working of the speech recognition system
Building a speech recognizer
Difficulties while developing a speech recognition system
Visualization of audio signals
Characterization of the audio signal
Monotone audio signal generation
Extraction of features from speech
Recognition of spoken words
Conclusion
Questions
8. Implementing Artificial Neural Network (ANN) with Python
Introduction
Structure
Objective
Understanding of Artificial Neural Network (ANN)
A biological neuron
Working of ANN
The basic structure of ANN
Types of ANNs
Optimizers for training the neural network
Gradient descent
Stochastic Gradient Descent (SGD)
Mini-Batch Gradient Descent
Stochastic Gradient Descent with Momentum
Adam (Adaptive Moment Estimation)
Regularization
Regularization techniques
Installing useful Python package for ANN
Examples of building some neural networks
Perceptron-based classifier
Single-layer neural networks
Multi-layer neural networks
Vector quantization
Conclusion
Questions
9. Implementing Reinforcement Learning with Python
Introduction
Structure
Objective
Understanding reinforcement learning
Workflow of reinforcement learning
Markov Decision Process (MDP)
Working of Markov Decision Process (MDP)
Difference between reinforcement learning and supervised learning
Implementing reinforcement learning algorithms
Reinforcement learning algorithms
Types of reinforcement learning
Benefits of reinforcement learning
Challenges with reinforcement learning
Building blocks of reinforcement learning
Agent
Environment
Constructing an environment using Python
Constructing an agent using Python
Conclusion
Questions
10. Implementing Deep Learning and Convolutional Neural Network
Introduction
Structure
Objective
Understanding Deep Learning
Machine learning versus deep learning
Elucidation of Convolutional Neural Networks
The Architecture of Convolutional Neural Network
Localization and object recognition with deep learning
Deep learning models
Image classification using CNN in Python
Conclusion
Questions
Index
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