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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!

The code bundle can be downloaded from here https://github.com/TrainingByPackt/Artificial-Intelligence-and-Machine-Learning-Fundamentals-eLearning

Downloading the example code for this course: You can download the example code files for all Packt video courses you have purchased from your account at http://www.PacktPub.com. If you purchased this course elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.

Table of Contents

  1. Chapter 1 : Principles of Artificial Intelligence
    1. Course Overview 00:10:41
    2. Installation and Setup 00:04:31
    3. Lesson Overview 00:03:21
    4. Introduction to AI and Machine Learning 00:08:14
    5. How Does AI Solve Real World Problems? 00:14:22
    6. Fields and Applications of Artificial Intelligence 00:08:31
    7. AI Tools and Learning Models 00:06:45
    8. The Role of Python in Artificial Intelligence 00:14:17
    9. A Brief Introduction to the NumPy Library 00:06:59
    10. Python for Game AI 00:11:52
    11. Breadth First Search and Depth First Search 00:13:58
    12. Lesson Summary 00:02:07
  2. Chapter 2 : AI with Search Techniques and Games
    1. Lesson Overview 00:11:05
    2. Heuristics 00:12:50
    3. Tic-Tac-Toe 00:10:04
    4. Pathfinding with the A* Algorithm 00:07:35
    5. Introducing the A* Algorithm 00:19:30
    6. Game AI with the Minmax Algorithm 00:09:35
    7. Game AI with Alpha-Beta Pruning 00:08:18
    8. Lesson Summary 00:01:24
  3. Chapter 3 : Regression
    1. Lesson Overview 00:02:35
    2. Linear Regression with One Variable 00:13:16
    3. Fitting a Model on Data with scikit-learn 00:13:51
    4. Linear Regression with Multiple Variables 00:10:41
    5. Preparing Data for Protection 00:09:15
    6. Polynomial and Support Vector Regression 00:13:38
    7. Lesson Summary 00:01:32
  4. Chapter 4 : Classification
    1. Lesson Overview 00:00:53
    2. The Fundamentals of Classification Part 1 00:06:36
    3. The Fundamentals of Classification Part 2 00:12:03
    4. The k-nearest neighbor Classifier 00:13:28
    5. Classification with Support Vector Machines 00:13:09
    6. Lesson Summary 00:01:35
  5. Chapter 5 : Using Trees for Predictive Analysis
    1. Lesson Overview 00:01:18
    2. Introduction to Decision Trees 00:14:03
    3. Entropy 00:07:30
    4. Gini Impurity 00:11:08
    5. Precision and Recall 00:15:34
    6. Random Forest Classifier 00:09:05
    7. Random Forest Classification Using scikit-learn 00:06:42
    8. Lesson Summary 00:01:31
  6. Chapter 6 : Clustering
    1. Lesson Overview 00:01:22
    2. Introduction to Clustering 00:11:28
    3. The k-means Algorithm 00:13:39
    4. Mean Shift Algorithm 00:12:58
    5. Lesson Summary 00:01:31
  7. Chapter 7 : Deep Learning with Neural Networks
    1. Lesson Overview 00:01:14
    2. TensorFlow for Python 00:13:07
    3. Introduction to Neural Networks 00:15:40
    4. Forward and Backward Propagation 00:14:12
    5. Training the TensorFlow Model 00:09:02
    6. Deep Learning 00:05:36
    7. Lesson Summary 00:03:09