The Machine Learning Fundamentals course introduces learners to the core concepts of machine learning and predictive analytics. Students will understand how machines learn from data, different types of learning algorithms, and real-world applications of machine learning. This course focuses on conceptual clarity with guided practical exposure, making it ideal for beginners entering the AI field.
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Course Syllabus
Module 1: Introduction to Machine Learning
What is Machine Learning
Artificial Intelligence vs Machine Learning
Real-World Applications of ML
ML in Industry
Module 2: Types of Machine Learning
Supervised Learning
Unsupervised Learning
Semi-Supervised Learning
Reinforcement Learning (Conceptual)
Module 3: Machine Learning Workflow
Problem Definition
Data Collection
Data Preparation
Model Building Overview
Module 4: Data Understanding & Preparation
Types of Data
Feature Selection Concepts
Data Cleaning Basics
Training & Testing Data
Module 5: Supervised Learning Algorithms
Linear Regression
Logistic Regression
k-Nearest Neighbors
Decision Trees
Module 6: Unsupervised Learning Algorithms
Clustering Concepts
K-Means Clustering
Hierarchical Clustering
Dimensionality Reduction Basics
Module 7: Model Evaluation Concepts
Accuracy & Error
Overfitting & Underfitting
Cross Validation
Performance Metrics
Module 8: Introduction to ML Tools
ML Libraries Overview
ML Development Workflow
Model Training Concepts
Model Prediction Basics
Module 9: Ethical AI & Limitations
Bias in Machine Learning
Ethical Considerations
Limitations of ML Models
Responsible AI Usage
Module 10: Real-World ML Use Cases
Recommendation Systems
Fraud Detection Overview
Predictive Analytics
Business Use Cases
Module 11: Practical Demonstrations
Simple ML Model Walkthrough
Training & Testing Demo
Result Interpretation
Guided Practical Session
Module 12: Final Assessment
Conceptual Evaluation
Scenario-Based Questions
Practical Assessment
Final Examination
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