Why This Course
Positioned as the practical foundation beneath the rest of the AI & Machine Learning academy — solid ML engineering before specializing into deep learning, NLP, or generative AI.
What You’ll Learn
- Data preprocessing and feature engineering
- Training and evaluating models with scikit-learn
- Cross-validation and avoiding overfitting
- Packaging and deploying a model behind an API
- Monitoring model performance over time
Curriculum
- ML Lifecycle Overview
- Data Preparation & Feature Engineering
- Supervised Learning Models
- Model Evaluation & Validation
- Unsupervised Learning Basics
- Model Packaging & Deployment
- Monitoring & Retraining
- Capstone: End-to-End ML Project
Who Should Attend
Developers and analysts building a foundation for ML/AI roles.
Prerequisites
Python fundamentals; basic statistics helpful.