AI & MLBeginner to Intermediate · 6 weeks
Machine Learning Foundations
A focused introduction to how machine learning actually works.
A standalone deep-dive into core ML concepts and algorithms, for learners who want a structured conceptual foundation before jumping into applied project work.
Self-PacedLive Online
Prerequisites
- —Basic Python
- —Comfort with basic algebra and statistics
What you'll be able to do
- ✓Explain the difference between supervised, unsupervised, and reinforcement learning
- ✓Implement common algorithms (regression, decision trees, clustering)
- ✓Evaluate model performance correctly
- ✓Avoid common beginner mistakes like overfitting
Curriculum
Core Concepts
- ·Types of machine learning
- ·The ML workflow end-to-end
- ·Bias, variance, and overfitting
Classic Algorithms
- ·Linear & logistic regression
- ·Decision trees and random forests
- ·k-means clustering
Evaluation & Iteration
- ·Train/test splits and cross-validation
- ·Choosing the right metric
- ·Iterating on a model
Format & Duration
Duration
6 weeks
Format
Self-Paced, Live Online
Level
Beginner to Intermediate
Ready to find your starting point?
Take the free 15-minute assessment — get your SkillShaft Score and see if this program fits where you are right now.
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