Artificial Intelligence and Machine Learning Fundamentals

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 alg

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Last updated Fri, 29-Jan-2021
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Course overview

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!


Target 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.


Business Outcomes 

  • 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

What will i 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
Requirements
Curriculum for this course
61 Lessons 7 hrs 48 mins
Artificial Intelligence and Machine Learning Fundamentals
1 Lessons 07:48:00 Hours
  • Artificial Intelligence and Machine Learning Fundamentals
    Preview 07:48:00
Lesson 1: Principles of Artificial Intelligence
13 Lessons
  • Course Overview
    Preview .
  • Installation and Setup
    Preview .
  • Lesson Overview
    Preview .
  • Introduction to AI and Machine Learning
    Preview .
  • How Does AI Solve Real World Problems?
    Preview .
  • Fields and Applications of Artificial Intelligence
    Preview .
  • AI Tools and Learning Models
    Preview .
  • The Role of Python in Artificial Intelligence
    Preview .
  • A Brief Introduction to the NumPy Library
    Preview .
  • Python for Game AI
    Preview .
  • Breadth First Search and Depth First Search
    Preview .
  • Lesson Summary
    Preview .
  • Assessment
    Preview .
Lesson 2: AI with Search Techniques and Games
9 Lessons
  • Lesson Overview
    Preview .
  • Heuristics
    Preview .
  • Tic-Tac-Toe
    Preview .
  • Pathfinding with the A* Algorithm
    Preview .
  • Introducing the A* Algorithm
    Preview .
  • Game AI with the Minmax Algorithm
    Preview .
  • Game AI with Alpha-Beta Pruning
    Preview .
  • Lesson Summary
    Preview .
  • Assessment
    Preview .
Lesson 3: Regression
8 Lessons
  • Lesson Overview
    Preview .
  • Linear Regression with One Variable
    Preview .
  • Fitting a Model on Data with scikit-learn
    Preview .
  • Linear Regression with Multiple Variables
    Preview .
  • Preparing Data for Protection
    Preview .
  • Polynomial and Support Vector Regression
    Preview .
  • Lesson Summary
    Preview .
  • Assessment
    Preview .
Lesson 4: Classification
7 Lessons
  • Lesson Overview
    Preview .
  • The Fundamentals of Classification Part 1
    Preview .
  • The Fundamentals of Classification Part 2
    Preview .
  • The k-nearest neighbor Classifier
    Preview .
  • Classification with Support Vector Machines
    Preview .
  • Lesson Summary
    Preview .
  • Assessment
    Preview .
Lesson 5: Using Trees for Predictive Analysis
9 Lessons
  • Lesson Overview
    Preview .
  • Introduction to Decision Trees
    Preview .
  • Entropy
    Preview .
  • Gini Impurity
    Preview .
  • Precision and Recall
    Preview .
  • Random Forest Classifier
    Preview .
  • Random Forest Classification Using scikit-learn
    Preview .
  • Lesson Summary
    Preview .
  • Assessment
    Preview .
Lesson 6: Clustering
6 Lessons
  • Lesson Overview
    Preview .
  • Introduction to Clustering
    Preview .
  • The k-means Algorithm
    Preview .
  • Mean Shift Algorithm
    Preview .
  • Lesson Summary
    Preview .
  • Assessment
    Preview .
Lesson 7: Deep Learning with Neural Networks
8 Lessons
  • Lesson Overview
    Preview .
  • TensorFlow for Python
    Preview .
  • Introduction to Neural Networks
    Preview .
  • Forward and Backward Propagation
    Preview .
  • Training the TensorFlow Model
    Preview .
  • Deep Learning
    Preview .
  • Lesson Summary
    Preview .
  • Assessment
    Preview .
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About instructor
Includes:
  • 7 hrs 48 mins On demand videos
  • 61 Lessons
  • Access on mobile and tv
  • Full lifetime access