This course provides a foundational understanding of machine learning concepts and techniques. Learn what machine learning is, how it differs from traditional programming, and the basic types of machine learning algorithms. Get acquainted with fundamental concepts like data, features, models, and evaluation. Build simple models for regression, classification, and clustering using practical examples.
Prerequisites Basic Python helpful
Level:
Core Concepts
Define machine learning and distinguish it from traditional programming.
ML Types
Identify and describe supervised, unsupervised, and reinforcement learning.
Data Handling
Understand the role of data, features, and labels in machine learning.
Basic Algorithms
Explain how simple algorithms like Linear Regression, KNN, and K-Means work.
Model Building
Outline the steps involved in training and evaluating a basic machine learning model.
Data Preparation
Perform basic data preprocessing tasks like handling missing values and scaling features.