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Artificial Intelligence in 3 Hours

Video Description

Get up and running with AI in less than 3 hours

About This Video

  • Understanding how to solve Supervised and Unsupervised tasks using Machine Learning and Deep Learning concepts
  • Understand the various aspects to be considered while deploying AI models
  • Understanding Reinforcement Learning and why is it so important for building AI systems

In Detail

What do self-driving cars, face recognition, web search, industrial robots, missile guidance, and tumor detection have in common? They are all complex real-world problems being solved with applications of intelligence (AI). This course provides you with a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems.

In this course, you will progress to advanced AI techniques and concepts, and work on real-life datasets on how training works, model evaluation and how to process their first dataset in Supervised and Unsupervised Learning. You will be introduced to neural networks, machine Learning, and Deep Learning. You will understand the concept of Artificial Intelligence and how it is applicable in the enterprise context. You will then cover the basics of machine learning relate how Deep Learning and AI fits with them

Finally, you will see understand, Deep Reinforcement Learning and will be able to implement AI in your applications.

The code files for this are present on GitHub at - https://github.com/elroyr/Artificial-Intelligence-in-3-hours-

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 : Understanding Supervised Machine Learning
    1. The Course Overview 00:03:22
    2. Breast Cancer Classification 00:01:37
    3. Breast Cancer Dataset Exploration 00:06:54
    4. Breast Cancer Dataset Preprocessing 00:06:39
    5. Intuition Behind K-Nearest Neighbors Algorithm 00:04:38
    6. Creating the KNN Algorithm 00:01:34
    7. Grid Search and Hyper Parameters 00:04:56
    8. Training the Model 00:02:07
    9. Model evaluation 00:05:51
    10. What Is Supervised Learning? 00:07:02
  2. Chapter 2 : Exploring Unsupervised Machine Learning
    1. Customer Segmentation 00:02:22
    2. Mall Customers Dataset Exploration 00:02:28
    3. Mall Customers Dataset Preprocessing 00:06:01
    4. Why Do We Split Dataset into Training and Testing Parts ? 00:03:55
    5. Intuition Behind K-Means Algorithm 00:05:16
    6. Creating K-Means Algorithm 00:01:50
    7. Training and Evaluating the KMeans Model 00:03:52
    8. Cluster Analysis 00:05:39
  3. Chapter 3 : Working with Deep Learning
    1. Boston Housing - Task Explanation 00:02:07
    2. Boston Housing Dataset Exploration 00:06:49
    3. Boston Housing Dataset Preprocessing 00:03:55
    4. Explaining the Fully Connected Network 00:09:38
    5. Building the Model 00:03:09
    6. Compiling the Model ( Choosing the Loss Function and Metrics) 00:03:08
    7. Training the Model 00:02:08
    8. Model Evaluation 00:02:39
  4. Chapter 4 : Image Classification with Convolutional Neural Networks
    1. Image Classification 00:02:14
    2. MNIST Dataset Exploration 00:04:46
    3. MNIST Dataset Preprocessing 00:05:15
    4. How Do Convolutional Neural Networks Work? 00:08:31
    5. Building the Model 00:04:13
    6. Compiling the Model( Choosing the Loss Function and Metrics) 00:01:23
    7. Training the Model 00:01:56
    8. Model Evaluation 00:02:34
    9. Explaining What Tasks Can Be Solved Using CNNs 00:02:48
  5. Chapter 5 : Working With Deep Reinforcement Learning
    1. Explaining What Reinforcement Learning Is 00:02:53
    2. States, Rewards, and Value 00:08:39
    3. Explaining the Deep Q Network 00:06:30
    4. Experience Replay 00:03:51
    5. What Is a Policy? 00:06:50
    6. Building the Model 00:02:14
    7. Compiling the Model 00:00:46
    8. Training the Model 00:02:26
    9. Game Playing with the Trained Model 00:04:03
    10. Real World Problems Solved with Reinforcement Learning 00:04:02