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

Description

Cover Title Page Copyright and Credits Packt Upsell Contributors Table of Contents Preface 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 Summary 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 Summary 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 Summary 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 Summary Chapter 5: Deep Learning Deep learning methods Convolutions and pooling Identifying handwritten mathematical symbols with CNNs Revisiting the bird species identifier to use images Summary Other Books You May Enjoy Index