Supervised & Unsupervised Learning

About Course

Learn how machines learn from labeled and unlabeled data through supervised and unsupervised learning techniques. This beginner-friendly course covers regression, classification, clustering, dimensionality reduction, model evaluation, data preparation, and practical applications to help learners understand how different Machine Learning approaches are used to solve real-world problems.

Understand the fundamentals of supervised and unsupervised learning
Learn the differences between labeled and unlabeled datasets
Understand regression and classification techniques
Explore clustering methods and their practical applications
Learn how to prepare, train, and test Machine Learning models
Understand model evaluation and performance metrics
Explore feature selection and dimensionality reduction concepts
Apply supervised and unsupervised learning techniques to practical problems

Course Content

Module 1: Foundations of Supervised and Unsupervised Learning
Introduction to Supervised Learning Introduction to Unsupervised Learning Labeled vs Unlabeled Data Training, Validation, and Test Data Regression vs Classification Choosing the Right Learning Approach

Module 2: Supervised Learning Techniques
Introduction to Linear Regression Understanding Logistic Regression Classification Concepts and Decision Boundaries Decision Trees and Basic Ensemble Methods Model Training and Prediction Evaluating Supervised Learning Models

Module 3: Unsupervised Learning Techniques
Introduction to Clustering K-Means Clustering Hierarchical Clustering Understanding Similarity and Distance Introduction to Dimensionality Reduction Practical Applications of Unsupervised Learning

Module 4: Model Evaluation and Practical Applications
Comparing Supervised and Unsupervised Models Accuracy, Precision, Recall, and F1 Score Understanding Overfitting and Underfitting Feature Selection and Data Preprocessing Real-World Applications and Case Studies Building a Practical Learning Workflow

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Supervised & Unsupervised Learning

About Course

What Will You Learn?

Course Content

Module 1: Foundations of Supervised and Unsupervised Learning
Introduction to Supervised Learning Introduction to Unsupervised Learning Labeled vs Unlabeled Data Training, Validation, and Test Data Regression vs Classification Choosing the Right Learning Approach

Module 2: Supervised Learning Techniques
Introduction to Linear Regression Understanding Logistic Regression Classification Concepts and Decision Boundaries Decision Trees and Basic Ensemble Methods Model Training and Prediction Evaluating Supervised Learning Models

Module 3: Unsupervised Learning Techniques
Introduction to Clustering K-Means Clustering Hierarchical Clustering Understanding Similarity and Distance Introduction to Dimensionality Reduction Practical Applications of Unsupervised Learning

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About Course

What Will You Learn?

Course Content

Module 1: Foundations of Supervised and Unsupervised Learning Introduction to Supervised Learning Introduction to Unsupervised Learning Labeled vs Unlabeled Data Training, Validation, and Test Data Regression vs Classification Choosing the Right Learning Approach

Module 2: Supervised Learning Techniques Introduction to Linear Regression Understanding Logistic Regression Classification Concepts and Decision Boundaries Decision Trees and Basic Ensemble Methods Model Training and Prediction Evaluating Supervised Learning Models

Module 3: Unsupervised Learning Techniques Introduction to Clustering K-Means Clustering Hierarchical Clustering Understanding Similarity and Distance Introduction to Dimensionality Reduction Practical Applications of Unsupervised Learning

Student Ratings & Reviews

Please Rotate Your Device

Module 1: Foundations of Supervised and Unsupervised Learning
Introduction to Supervised Learning Introduction to Unsupervised Learning Labeled vs Unlabeled Data Training, Validation, and Test Data Regression vs Classification Choosing the Right Learning Approach

Module 2: Supervised Learning Techniques
Introduction to Linear Regression Understanding Logistic Regression Classification Concepts and Decision Boundaries Decision Trees and Basic Ensemble Methods Model Training and Prediction Evaluating Supervised Learning Models

Module 3: Unsupervised Learning Techniques
Introduction to Clustering K-Means Clustering Hierarchical Clustering Understanding Similarity and Distance Introduction to Dimensionality Reduction Practical Applications of Unsupervised Learning