What You Will Master in This Track
Foundational Principles
Master the underlying algorithms, architectures, and theoretical intuition powering modern artificial intelligence.
Workflow & Agent Modeling
Understand how autonomous agents perceive data, represent domain knowledge, and execute goal-driven decisions.
Practical Real-World Use
Bridge concept with practice through real business case studies, hands-on exercises, and domain workflows.
Responsible & Ethical AI
Mitigate model bias, ensure transparency, and deploy accountable AI solutions that adhere to industry governance.
Course Curriculum & Modules (24)
Select any module below to inspect in-depth syllabus notes, practical focus, and review questions.
01
Machine Learning Basics
02
Types of Machine Learning
03
Supervised Learning
04
Unsupervised Learning
05
Reinforcement Learning
06
Classification
07
Regression
08
Clustering
09
K-Nearest Neighbors (KNN)
10
Decision Trees
11
Random Forest
12
Ensemble Learning
13
Support Vector Machine (SVM)
14
Feature Engineering
15
Data Preparation
16
Model Training
17
Model Testing
18
Model Evaluation
19
Cross-Validation
20
Hyperparameter Tuning
21
Machine Learning Applications
22
Machine Learning Project
23
Model Training & Testing
24