LEVEL
Practical

DURATION
8 hours per module

TRAINER
CIM4.0

MODE
In the presence

TRAINEES
Max 10 per company

PRICE

Description

AI Practical is a comprehensive, hands-on course structured to learn how to develop AI-based applications by leveraging existing libraries and frameworks.

The course consists of three modules that can be configured according to one's needs and can be taken individually.

Module 1: INTRODUCTION TO PYTHON FOR AI.

Module 2: COMPUTER VISION AND ARTIFICIAL INTELLIGENCE.

Module 3: ARTIFICIAL INTELLIGENCE FOR DATA ANALYSIS.

Macro Topics

Module 1: INTRODUCTION TO PYTHON FOR AI.

• Goal: To provide the basics of the Python programming language for AI applications.
• Program:

  • Introduction to Python
  • Basic concepts of Python
  • Data structures and file management
  • Libraries for Artificial Intelligence.

Module 2: COMPUTER VISION AND ARTIFICIAL INTELLIGENCE.

• Goal: To lay the foundation for understanding the operation and potential of neural networks used in the field of computer vision, and to give the tools for designing and testing these systems in the real world.
• Program:

  • Introduction

    • Neural Networks
    • Cost function and training

  • Computer Vision

    • Tasks in Computer Vision
    • Feature extraction

  • Convolutional Neural Networks (CNNs)

    • Architecture
    • Convolutional layers
    • Trigger functions and hyperparameters

  • Exercises

    • Object Detection with YOLO
    • Training on custom dataset

Module 3: ARTIFICIAL INTELLIGENCE FOR DATA ANALYSIS.

• Aim: To apply machine learning and deep learning techniques to data analysis and Natural Language Processing (NLP) to extract information, predictions, and insights from structured and unstructured data.
• Program:

  • Data Analysis in Python

    • Data management and manipulation
    • Data visualization
    • Example: price of gasoline in Italy

  • Introduction to data analysis with AI

    • Supervised and unsupervised learning algorithms
    • Linear Regression and K-Nearest Neighbors
    • Decision trees and clustering
    • NLP

  • Deep Learning

    • Artificial neural networks and deep learning
    • Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)
    • Generative Adversarial Networks (GAN)

  • Practical exercise: predicting a stock price based on historical data

Trainee’s Profile

Technicians from companies that want to work with AI.

Pre-Requirements

Prerequisites Module 1: Programming basics or none;

Prerequisites Modules 2 and 3: Basic knowledge of Python programming and theoretical knowledge of AI;

Objectives

The course aims to practice Artificial Intelligence concepts, from Classical Machine Learning to Deep Learning, in order to build applications by leveraging existing libraries and frameworks.

More info

English language available upon request.
The subscription must be carried out within the fifth working day preceding the course starting date.