Learning models implementation

Discover the good practices related to machine learning with this training. See also how to properly handle the data collected.
Private session

This training is available in a private or personalized format. It can be provided in one of our training centres or at your offices. Call one of our consultants of submit a request online.

Call now at 1 877 624.2344

  • Duration: 2 days
  • Regular price: On request

Course outline

Duration : 2 days

In this training, you will learn the basics of machine learning and of its subdomains, as well as techniques for analyzing and preprocessing data. Understanding will be reinforced through practical examples and case scenarios that will highlighting the reality of today's AI technologies, their various uses, and the importance of data.

This practice-oriented training provides techniques to quickly and efficiently apply machine learning algorithms to solve a business need. We will be using Python programming language to realize programs.

Audience

Developers wanting to learn the practical basics of machine learning.

Prerequisites

Basic knowledge in programming

Objectives

Machine Learning

  • Understand machine learning and its subdomains
  • Acquire common machine learning algorithms
  • Be able to choose the appropriate algorithm to suit your need
  • Develop the reflex to analyse data and algorithm results
Data
  • Learn about data collection and preprocessing
  • Learn about data analysis
Business Need
  • Based on case scenarios, be able to understand the business need and implement the experimental protocol in order to produce a performant learning model
Test
  • Know how to validate a machine learning model and to analyze its performance
Python
  • Know how to prepare data for machine learning algorithms
  • Learn to use the major machine learning libraries
  • Know how to produce learning algorithm performance reports

Contents

Part One

  • Introduction to Artificial Intelligence and Machine Learning
  • Overview of supervised learning
  • Overview of unsupervised learning
  • Overview of reinforcement learning
  • Supervised learning: classification and regression (Part 1)
  • Preparing data
  • Some supervised learning algorithms
  • Training, validation and testing of learning models
  • Model evaluation and performance analysis
  • Q & A
Part Two
  • Supervised learning: classification and regression (Part 2)
  • Interpretability of learning models
  • Correlation and causality
  • Visualization of learning models
  • Importance of attributes
  • Strategies to improve learning models
  • Real case study
  • Application of acquired knowledge to a real problem
  • Q & A

Surround yourself with the best

Pierre-Edouard Brondel
Pierre-Edouard Brondel
Trainer and Desktop Application Consultant
Renowned as an educational expert in the IT and office technology field who has accumulated more than 25 years of experience, Pierre-Édouard is first and foremost passionate about human capital.
Marc Maisonneuve
Marc Maisonneuve
Trainer and Professional Efficiency Consultant
Frédéric Paradis
Frédéric Paradis
Certified Trainer and Cloud Architect
As a certified Microsoft trainer, Frédéric describes himself as a Cloud magician who easily navigates the mythical space between technology and reality.
Luc Labelle
Certified Trainer and IT Consultant
Recognized by his peers as an inspiring coach, trainer, and consultant, Luc is able to transfer his knowledge to benefit his teams.
Be aware of trends, innovations and best practices, every month.
Confidentiality
Training center accredited by Emploi-Québec, Accreditation : 0051460
GST : 141582528 – QST : 1019557738
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