Introduction
Machine Learning (ML) is a core pillar of Artificial Intelligence, allowing systems to learn patterns and make decisions from data. This 5-day course introduces the foundational algorithms, workflows, and evaluation techniques used in ML. Participants will learn how models are trained, validated, and optimized for different types of tasks. Real-world examples will help clarify when and why specific ML methods are applied. By the end, learners will be able to build and assess basic machine learning models.
Course Objectives
- Understand key ML algorithms and concepts
- Learn how to prepare and clean datasets
- Build and evaluate ML models using standard workflows
- Develop intuition for choosing appropriate algorithms
- Gain hands-on experience with supervised and unsupervised learning
Target Audience
- Beginners with basic technical knowledge
- Data analysts transitioning into ML
- Business professionals seeking ML literacy
- Developers wanting foundational ML skills
- Students exploring data science or AI
Course Outline
- 5 Sections
- 0 Lessons
- 5 Days
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- Day 1: ML Concepts & Terminology• What is machine learning?
• Types of learning
• Model training pipeline
• Understanding bias/variance
• Real-world ML use cases0 - Day 2: Working With Data• Data cleaning and transformation
• Handling missing values
• Feature engineering
• Feature selection basics
• Exercise: Prepare a dataset0 - Day 3: Supervised Learning• Regression vs. classification
• Linear models
• Decision trees
• Support Vector Machines
• Hands-on: Build a classifier0 - Day 4: Unsupervised Learning• Clustering basics
• Dimensionality reduction
• Anomaly detection
• K-Means and PCA
• Hands-on: Clustering exercise0 - Day 5: Model Evaluation & Optimization• Accuracy, precision, recall, F1
• Cross-validation
• Hyperparameter tuning
• Avoiding overfitting
• Capstone project0
Instructor
Lessons:
