Machine Learning for Absolute Beginners – Level 1 Course Syllabus

Overview: This course is designed for absolute beginners with no prior experience in machine learning. It offers a step-by-step introduction to core concepts and practical skills through hands-on projects. The curriculum spans approximately 10 hours of content, divided into five structured modules that build foundational knowledge and culminate in real-world applications. Each module combines clear explanations with practical exercises to ensure understanding and retention. By the end, learners will be equipped to build, evaluate, and interpret basic machine learning models.

Module 1: Introduction to Machine Learning

Estimated time: 1 hours

• Overview of machine learning and its significance in data science
• Understanding the role of machine learning in modern technology
• Differentiating between supervised and unsupervised learning
• Exploring real-world applications of machine learning

Module 2: Data Preprocessing

Estimated time: 2 hours

• Techniques for handling missing data
• Identifying and managing outliers
• Normalization and standardization of data
• Splitting data into training and testing sets

Module 3: Supervised Learning Algorithms

Estimated time: 3 hours

• Implementing Linear Regression for continuous data prediction
• Applying K-Nearest Neighbors for classification tasks
• Understanding the working principles of supervised algorithms
• Comparing use cases for different supervised models

Module 4: Model Evaluation

Estimated time: 2 hours

• Using Mean Squared Error (MSE) and R-squared for regression models
• Evaluating classification models with accuracy, precision, recall, and F1-score
• Interpreting evaluation metrics to improve model performance

Module 5: Practical Applications

Estimated time: 2 hours

• Applying machine learning models to real-world datasets
• Building simple machine learning projects
• Reinforcing learning through hands-on implementation

Module 6: Final Project

Estimated time: 1 hours

• Select a dataset and define a prediction problem
• Preprocess data and train a supervised learning model
• Evaluate model performance and present results

Prerequisites

• Basic computer literacy
• No prior programming or machine learning experience required
• Familiarity with simple mathematical concepts (e.g., averages, percentages)

What You'll Be Able to Do After

• Explain the fundamentals of machine learning and its applications
• Preprocess and prepare data for machine learning models
• Implement Linear Regression and K-Nearest Neighbors algorithms
• Evaluate model performance using standard metrics
• Apply machine learning to real-world problems through hands-on projects