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.
Moyens d'évaluation :
- Quiz pré-formation de vérification des connaissances (si applicable)
- Évaluations formatives pendant la formation, à travers les travaux pratiques réalisés sur les labs à l’issue de chaque module, QCM, mises en situation…
- Complétion par chaque participant d’un questionnaire et/ou questionnaire de positionnement en amont et à l’issue de la formation pour validation de l’acquisition des compétences
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
Italien
Italie
Moyens Pédagogiques :