Machine Learning 101

Description

This beginner-friendly course introduces the core principles of machine learning. Participants will learn how to use Python and Scikit-Learn to build and evaluate regression and classification models, gaining hands-on experience with real-world data and essential ML workflows.

Indicative Duration: 8 training hours
*Duration is adjusted based on the final scope and the target audience.

Scope

1. Preliminaries
  • Introduction to Maching Learning,
  • Python – Data Structures and Libraries
2. Preprocessing
  • Load data and Preprocessing – Intro to Scikit-Learn
3. ML Algorithms
  • Machine Learning step-by-step using Linear Regression
  • Regression metrics
4. More Algorithms
& Model evaluation
  • Classification: Logistic Regression, kNN, Decision Trees,
  • Confusion Matrix, Metrics

Learning Objectives

Upon completion of the course participants will be able to:

  1. Understand the basic concepts and goals of machine learning.
  2. Work with Python data structures and essential ML libraries.
  3. Load and preprocess datasets using Scikit-Learn.
  4. Apply linear regression and evaluate regression models.
  5. Implement basic classification algorithms and assess model performance using appropriate metrics.

Target Audience

  • Roles: Aspiring Data Scientists, Junior ML Engineers, Analysts, Developers entering ML
  • Seniority Levels: Entry-level to Junior professionals with minimal or no prior ML experience

Prerequisite Knowledge

  • Basic Python programming skills
  • Familiarity with Jupyter notebooks or similar environments
  • General understanding of data analysis concepts

Delivery Method

Sessions can be delivered via the following formats:

  • Live Online – Interactive virtual sessions via video conferencing
  • On-Site – At your organization’s premises
  • In-Person – At Code.Hub’s training center
  • Hybrid – A combination of online and in-person sessions

 

The training methodology combines presentations, live demonstrations, hands-on exercises and interactive discussions to ensure participants actively practice AI in realistic work scenarios.

Tags:

Date

On Demand

Organizer

Code.Hub
Email
[email protected]