Artificial Intelligence and Machine Learning Fundamentals (LO-AIMLF)

 

Course Overview

Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples.

As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore's law.

Course Objectives

  • Understand the importance, principles, and fields of AI
  • 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
  • Carry out clustering using the k-means and mean shift algorithms
  • Understand the fundamentals of deep learning via practical examples

Course Content

1: Principles of Artificial Intelligence
  • Introduction
  • Fields and Applications of Artificial Intelligence
  • AI Tools and Learning Models
  • The Role of Python in Artificial Intelligence
  • Python for Game AI
  • Summary
2: AI with Search Techniques and Games
  • Introduction
  • Heuristics
  • Pathfinding with the A* Algorithm
  • Game AI with the Minmax Algorithm and Alpha-Beta Pruning
  • Summary
3: Regression
  • Introduction
  • Linear Regression with One Variable
  • Linear Regression with Multiple Variables
  • Polynomial and Support Vector Regression
  • Summary
4: Classification
  • Introduction
  • The Fundamentals of Classification
  • Classification with Support Vector Machines
  • Summary
5: Using Trees for Predictive Analysis
  • Introduction to Decision Trees
  • Random Forest Classifier
  • Summary
6: Clustering
  • Introduction to Clustering
  • The k-means Algorithm
  • Mean Shift Algorithm
  • Summary
7: Deep Learning with Neural Networks
  • Introduction
  • TensorFlow for Python
  • Introduction to Neural Networks
  • Deep Learning
  • Summary
8: Appendix A
  • Lesson 1: Principles of AI
  • Lesson 2: AI with Search Techniques and Games
  • Lesson 4: Classification
  • Lesson 5: Using Trees for Predictive Analysis
  • Lesson 6: Clustering
  • Lesson 7: Deep Learning with Neural Networks

Prices & Delivery methods

Online Training

Duration
3 days

Classroom Training

Duration
3 days

Schedule

FLEX Classroom Training (hybrid course):   Course participation either on-site in the classroom or online from the workplace or from home.

Italian

Time zone: Central European Time (CET)   ±1 hour

Online Training This is a FLEX course. Time zone: Central European Time (CET)
Online Training This is a FLEX course. Time zone: Central European Summer Time (CEST)
Online Training This is a FLEX course. Time zone: Central European Summer Time (CEST)
Online Training This is a FLEX course. Time zone: Central European Time (CET)
FLEX Classroom Training (hybrid course):   Course participation either on-site in the classroom or online from the workplace or from home.

Europe

Italy

Rome This is a FLEX course in Italian language. Time zone: Central European Time (CET) Course language: Italian
Sesto San Giovanni (MI) This is a FLEX course in Italian language. Time zone: Central European Summer Time (CEST) Course language: Italian
Rome This is a FLEX course in Italian language. Time zone: Central European Summer Time (CEST) Course language: Italian
Sesto San Giovanni (MI) This is a FLEX course in Italian language. Time zone: Central European Time (CET) Course language: Italian

If you can't find a suitable date, don't forget to check our world-wide FLEX training schedule.