Machine Learning Fundamentals & Applications

Overview

Course Description

Comprehensive machine learning course for professionals transitioning from analytics to ML. Covers theory and practice, with emphasis on real-world applications and responsible AI.

Prerequisites: Python for Data Analytics (Course 3.2) or equivalent.

Career Relevance

Jobs: Machine Learning Engineer, Data Scientist, ML Consultant (₦5-10M annually)
Next step: Data Science & AI Specialist, Advanced ML specialization
High demand in tech, finance, e-commerce

Module 1: ML Fundamentals & Problem Setup
Supervised vs unsupervised learning

Training, validation, test splits

Overfitting and underfitting

Cross-validation

Practice Project
Module 2: Regression & Classification Models
Linear and logistic regression

Decision trees

Ensemble methods (Random Forest, Gradient Boosting)

Hyperparameter tuning

Practice Project
Module 3: Unsupervised Learning & Feature Engineering
Clustering (K-means, hierarchical)

Dimensionality reduction (PCA)

Feature selection techniques

Creating new features

Practice Project
Module 4: Model Evaluation & Responsible ML
Classification metrics (precision, recall, F1)

Regression metrics (RMSE, R²)

ROC curves and thresholds

Bias and fairness

Explainability (SHAP, LIME)

Practice Project
Module 5: Prompt Engineering & Deployment
Using AI to generate ML code

Model deployment basics

Monitoring models in production

Practice: AI-assisted model development
Module 6: Capstone & Portfolio
Complete ML project

Portfolio Project

Level:        Advanced
Duration:     6 weeks
Physical Fee: ₦70,000
Virtual Fee:  ₦56,000

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Machine Learning Fundamentals & Applications

Course Description

Career Relevance

Certification Requirements

Vision Statement:

Mission Statement: