Duration 3 days – 21 hrs
Overview
This course provides a beginner-friendly introduction to the core concepts of machine learning (ML). Participants will learn the differences between supervised and unsupervised learning, explore fundamental algorithms like regression and classification, and get hands-on practice using Python’s powerful scikit-learn library to build simple ML models.
Objectives
- Understand the basic concepts and categories of machine learning.
- Distinguish between supervised and unsupervised learning.
- Implement simple regression and classification models.
- Use scikit-learn to build, train, and evaluate machine learning models.
- Gain confidence to move forward into deeper ML and AI studies.
Audience
- Beginners with a basic understanding of Python programming who wants to enter the AI and machine learning field.
- Aspiring AI practitioners, data scientists, data scientists, AI engineers, and analysts
- and software developers.
- Students, IT professionals, and analysts looking to transition into machine learning.
- Business professionals and technical managers interested in understanding how ML works to support data-driven decision-making
Prerequisites
- Basic Python programming knowledge and a general understanding of data structures (lists, loops, and functions).
Course Content
Day 1: Machine Learning Fundamentals
- What is Machine Learning?
- Supervised vs Unsupervised Learning explained
- Real-world examples and use cases
- Introduction to machine learning workflow (data → model → prediction)
Day 2: Key ML Tasks – Regression and Classification
- Regression fundamentals:
- Predicting continuous outcomes (e.g., house prices)
- Simple Linear Regression with scikit-learn
- Classification fundamentals:
- Predicting categories (e.g., spam or not spam)
- Logistic Regression basics
- Evaluating model performance (mean squared error, accuracy, confusion matrix)
Day 3: Practical Tools and Hands-on Practice with scikit-learn
- Introduction to scikit-learn: key features and architecture
- Loading and splitting datasets
- Training and testing models
- Model evaluation and simple hyperparameter tuning
- End-to-end mini project: build a regression or classification model from scratch
Final Hands-On Activity:
- Mini project: Load a dataset, choose between regression/classification, train a model using scikit-learn, and evaluate performance.
