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