Supervised Learning: Classification in Machine Learning | AIML End-to-End Session 44
Ready to dive deep into the world of
Artificial Intelligence
Machine Learning (AIML)?
Welcome to Session 44 of our End-to-End AIML series! In this session, we focus on Supervised Learning with an emphasis on Classification, a key technique in machine learning used to categorize data into predefined classes. Classification is widely used in various real-world applications such as spam detection, image recognition, and medical diagnoses.
What You'll Learn:
Introduction to Classification: Understand the basics of supervised learning and how classification differs from regression.
Types of Classification Algorithms: Explore popular algorithms such as:
Logistic Regression
K-Nearest Neighbors (KNN)
Decision Trees
Random Forest
Support Vector Machines (SVM)
Naive Bayes
Binary vs. Multi-Class Classification: Learn the difference between binary classification and multi-class classification and when to use each.
Evaluation Metrics for Classification: Understand how to evaluate classification models using metrics like:
Accuracy
Precision
Recall
F1 Score
Confusion Matrix
Hands-On Coding Examples: Build classification models using Scikit-learn in Python with practical examples and datasets.
Real-World Applications: See how classification algorithms are applied in industries such as finance, healthcare, marketing, and more.
This session is ideal for anyone looking to master classification techniques and apply them to real-world machine learning projects.
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