Artificial Intelligence and Machine Learning Fundamentals

Video description

Learn to develop real-world applications powered by the latest advances in intelligent systems

About This Video

  • Includes practical examples that explain key machine learning algorithms
  • Explains neural networks in detail with interesting example problems
  • Provides ample practice in applying AI with Python

In Detail

Machine learning and neural networks are fast becoming pillars on which you can build intelligent applications. The course will begin by introducing you to Python and discussing using AI search algorithms. You will learn math-heavy topics, such as regression and classification, illustrated by Python examples.

You will then progress on to advanced AI techniques and concepts, and work on real-life data sets to form decision trees and clusters. You will be introduced to neural networks, which is a powerful tool benefiting from Moore's law applied on 21st-century computing power. By the end of this course, you will feel confident and look forward to building your own AI applications with your newly-acquired skills!

Audience

This course is ideal for software developers and data scientists, who want to enrich their projects with machine learning. You do not need any prior experience in AI. We recommend that you have knowledge of high school level mathematics and at least one programming language, preferably Python.

Publisher resources

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Table of contents

  1. Chapter 1 : Principles of Artificial Intelligence
    1. Course Overview
    2. Installation and Setup
    3. Lesson Overview
    4. Introduction to AI and Machine Learning
    5. How Does AI Solve Real World Problems?
    6. Fields and Applications of Artificial Intelligence
    7. AI Tools and Learning Models
    8. The Role of Python in Artificial Intelligence
    9. A Brief Introduction to the NumPy Library
    10. Python for Game AI
    11. Breadth First Search and Depth First Search
    12. Lesson Summary
  2. Chapter 2 : AI with Search Techniques and Games
    1. Lesson Overview
    2. Heuristics
    3. Tic-Tac-Toe
    4. Pathfinding with the A* Algorithm
    5. Introducing the A* Algorithm
    6. Game AI with the Minmax Algorithm
    7. Game AI with Alpha-Beta Pruning
    8. Lesson Summary
  3. Chapter 3 : Regression
    1. Lesson Overview
    2. Linear Regression with One Variable
    3. Fitting a Model on Data with scikit-learn
    4. Linear Regression with Multiple Variables
    5. Preparing Data for Protection
    6. Polynomial and Support Vector Regression
    7. Lesson Summary
  4. Chapter 4 : Classification
    1. Lesson Overview
    2. The Fundamentals of Classification Part 1
    3. The Fundamentals of Classification Part 2
    4. The k-nearest neighbor Classifier
    5. Classification with Support Vector Machines
    6. Lesson Summary
  5. Chapter 5 : Using Trees for Predictive Analysis
    1. Lesson Overview
    2. Introduction to Decision Trees
    3. Entropy
    4. Gini Impurity
    5. Precision and Recall
    6. Random Forest Classifier
    7. Random Forest Classification Using scikit-learn
    8. Lesson Summary
  6. Chapter 6 : Clustering
    1. Lesson Overview
    2. Introduction to Clustering
    3. The k-means Algorithm
    4. Mean Shift Algorithm
    5. Lesson Summary
  7. Chapter 7 : Deep Learning with Neural Networks
    1. Lesson Overview
    2. TensorFlow for Python
    3. Introduction to Neural Networks
    4. Forward and Backward Propagation
    5. Training the TensorFlow Model
    6. Deep Learning
    7. Lesson Summary

Product information

  • Title: Artificial Intelligence and Machine Learning Fundamentals
  • Author(s): Zsolt Nagy
  • Release date: March 2019
  • Publisher(s): Packt Publishing
  • ISBN: 9781789953671