MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, Stereo | Duration: 2h 2m | 503 MB

Genre: eLearning | Language: English

Enter and explore the fascinating world of intelligent applications with Artificial Intelligence using the Python programming language

Considered the Holy Grail of automation, data analysis, and robotics, Artificial Intelligence has taken the world by storm as a major field of research and development. Python has surfaced as a dominate language in AI/ML programming because of its simplicity and flexibility, in addition to its great support for open source libraries such as spaCy and TensorFlow.

This video course is built for those with a basic understanding of artificial intelligence, introducing them to advanced artificial intelligence projects as they go ahead. The first project introduces natural language processing including part-of-speech tagging and named entity extraction. Wikipedia articles are used to demonstrate the extraction of keywords, and the Enron email archive is mined for mentions and relationships of people, places, and organizations. The spaCy library is used. The next project introduces genetic algorithms. The DEAP library is used. A music data set is used in a genetic algorithm that generates a music playlist satisfying multiple criteria such as song similarity and playlist length. The last project introduces reinforcement learning and deep reinforcement learning. The OpenAI Gym platform and Q-learning algorithm are used to build a game-playing AI.

The code bundle for this video course is available at -

https://github.com/PacktPublishing/Advanced-Artificial-Intelligence-Projects-with-Python

Style and Approach

Built for AI enthusiasts, machine learning and deep learning students, and developers across three realistic projects, this course covers modern techniques that will take your AI game with Python to the next level.

What You Will Learn

Extract names, places, and more and their relationships from text

Build a recommendation engine for finding new music

Use deep reinforcement learning to build an AI that plays arcade games

Employ the SpaCy and textacy libraries for natural language processing

Use popular libraries such as Keras and TensorFlow for reinforcement learning

Table of Contents

NATURAL LANGUAGE PROCESSING

GENETIC ALGORITHMS

LET’S PLAY A GAME – REINFORCEMENT LEARNING