Python Artificial Intelligence Projects for Beginners: Get up and running with Artificial Intelligence using 8 smart and exciting AI applications -

----------------------------------------------

Book Description

----------------------------------------------

This book begins with helping you to build your first prediction model using the popular Python library, scikit-learn. You will understand how to build a classifier using an effective machine learning technique, random forest, and decision trees. With exciting projects on predicting bird species, analyzing student performance data, song genre identification, and spam detection, you will learn the fundamentals and various algorithms and techniques that foster the development of these smart applications. In the concluding chapters, you will also understand deep learning and neural network mechanisms through these projects with the help of the Keras library.

By the end of this book, you will be confident in building your own AI projects with Python and be ready to take on more advanced projects as you progress

What you will learn

- Build a prediction model using decision trees and random forest

- Use neural networks, decision trees, and random forests for classification

- Detect YouTube comment spam with a bag-of-words and random forests

- Identify handwritten mathematical symbols with convolutional neural networks

- Revise the bird species identifier to use images

- Learn to detect positive and negative sentiment in user reviews

----------------------------------------------

Table of Contents

----------------------------------------------

Chapter 1: Building Your Own Prediction Models

- Classification overview and evaluation techniques Evaluation

- Decision trees

- Common APIs for scikit-learn classifiers

- Prediction involving decision trees and student performance data

Chapter 2: Prediction with Random Forests

- Random forests

- Usage of random forest

- Predicting bird species with random forests

- Making a confusion matrix for the data

Chapter 3: Applications for Comment Classification

- Text classification.

- Machine learning techniques

- Bag of words

- Detecting YouTube comment spam

- Word2Vec models

- Doc2Vec; Document vector

- Detecting positive or negative sentiments in user reviews

Chapter 4: Neural Networks

- Understanding neural networks

- Feed-forward neural networks

- Identifying the genre of a song with neural networks

- Revising the spam detector to use neural networks

Chapter 5: Deep Learning

- Deep learning methods

- Convolutions and pooling

- Identifying handwritten mathematical symbols with CNNs

- Revisiting the bird species identifier to use images