Machine Learning Foundations

Machine Learning Foundations

Duration: 3 Days

Description

Most machine learning courses either drown participants in linear algebra or wave their hands over how a model actually learns — this one does neither. It builds a practical, applied foundation: the major algorithm families and when each one fits the problem in front of you, the real workflow from raw data to a trained, evaluated model, and the mistakes that quietly produce a model that looks great in testing and falls apart on real data (leakage, overfitting, evaluating on the wrong metric). The goal throughout is working intuition participants can apply immediately, not theory they’d need a math degree to connect back to practice.
Class time is divided between instructor-led explanation (45 percent) and hands-on exercises (55 percent).

Audience

This course is for developers, data practitioners, and technical professionals starting a deliberate move into machine learning or data engineering. Basic programming ability is assumed, but no prior ML experience or advanced math background is required — that’s the specific gap this course is built to close.

Objectives

  • Explain core machine learning concepts and terminology
  • Describe the main types of ML and when each applies
  • Prepare and explore data for modeling
  • Train, tune, and evaluate models using standard tools
  • Interpret evaluation metrics and avoid common pitfalls
  • Understand where ML fits relative to generative AI

Prerequisites

No prior ML experience is required. The following will help:

  • Working knowledge of Python (see Python Foundations)
  • Comfort with basic data manipulation

Related AI Courses

See the full AI training roadmap and course directory for how this fits into a broader learning path.

New to Python first? Start with Python Foundations. Once you have ML fundamentals down, natural next steps include Data Engineering for the data-pipeline side, or MLOps & LLMOps for deploying and operating models in production.

Course Outline

  • Module 1 – Course Introduction
    • Welcome and objectives
    • What machine learning is and is not
    • ML, deep learning, and generative AI in context
  • Module 2 – The ML Landscape
    • Supervised, unsupervised, and reinforcement learning
    • Classification, regression, and clustering
    • How models learn from data
    • The end-to-end ML workflow
  • Module 3 – Working with Data
    • Data collection and exploration
    • Cleaning and preparing data
    • Features and feature engineering
    • Training, validation, and test splits
  • Module 4 – Building Models
    • Common algorithms and when to use them
    • Training a model with standard tools
    • Hyperparameters and tuning
    • Hands-on exercise: train your first model
  • Module 5 – Evaluating Models
    • Evaluation metrics for different tasks
    • Overfitting, underfitting, and generalization
    • Validation strategies
    • Interpreting and communicating results
  • Module 6 – From Model to Practice
    • Common pitfalls and how to avoid them
    • Bias, fairness, and data quality
    • Where models go next: deployment overview
    • Hands-on exercise: an end-to-end mini project
  • Module 7 – Course Wrap-Up
    • Key takeaways
    • Resources for continued learning
    • Next steps in the data and ML path