English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 7h 48m | 1.13 GB
Learn to develop real-world applications powered by the latest advances in intelligent systems
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!
What You Will Learn
- Understand the importance, principles, and fields of AI
- Learn to implement basic artificial intelligence concepts with Python
- Apply regression and classification concepts to real-world problems
- Perform predictive analysis using decision trees and random forests
- Perform clustering using the k-means and mean shift algorithms
- Understand the fundamentals of deep learning via practical examples
Table of Contents
01 Course Overview
02 Installation and Setup
03 Lesson Overview
04 Introduction to AI and Machine Learning
05 How Does AI Solve Real World Problems
06 Fields and Applications of Artificial Intelligence
07 AI Tools and Learning Models
08 The Role of Python in Artificial Intelligence
09 A Brief Introduction to the NumPy Library
10 Python for Game AI
11 Breadth First Search and Depth First Search
12 Lesson Summary
13 Lesson Overview
14 Heuristics
15 Tic-Tac-Toe
16 Pathfinding with the A_ Algorithm
17 Introducing the A_ Algorithm
18 Game AI with the Minmax Algorithm
19 Game AI with Alpha-Beta Pruning
20 Lesson Summary
21 Lesson Overview
22 Linear Regression with One Variable
23 Fitting a Model on Data with scikit-learn
24 Linear Regression with Multiple Variables
25 Preparing Data for Protection
26 Polynomial and Support Vector Regression
27 Lesson Summary
28 Lesson Overview
29 The Fundamentals of Classification Part 1
30 The Fundamentals of Classification Part 2
31 The k-nearest neighbor Classifier
32 Classification with Support Vector Machines
33 Lesson Summary
34 Lesson Overview
35 Introduction to Decision Trees
36 Entropy
37 Gini Impurity
38 Precision and Recall
39 Random Forest Classifier
40 Random Forest Classification Using scikit-learn
41 Lesson Summary
42 Lesson Overview
43 Introduction to Clustering
44 The k-means Algorithm
45 Mean Shift Algorithm
46 Lesson Summary
47 Lesson Overview
48 TensorFlow for Python
49 Introduction to Neural Networks
50 Forward and Backward Propagation
51 Training the TensorFlow Model
52 Deep Learning
53 Lesson Summary
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