Classification | Free AI Engineer Bootcamp Day 5 | Chitra Karanam | Techonquer

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Welcome to Day 5 of the FREE AI Engineer Bootcamp by Techonquer!

In today's session, we will explore Classification, one of the most important supervised Machine Learning techniques used to categorize data into predefined classes. Classification powers spam detection, fraud detection, malware detection, medical diagnosis, sentiment analysis, facial recognition, and countless AI-driven applications.

This session is designed to help beginners understand classification from the ground up while also covering practical implementation using Python and real-world datasets.

What You Will Learn Today:

✅ Introduction to Classification
✅ What is Classification in Machine Learning?
✅ Regression vs Classification
✅ Types of Classification
✅ Binary Classification
✅ Multi-Class Classification
✅ Multi-Label Classification
✅ Classification Workflow
✅ Logistic Regression
✅ K-Nearest Neighbors (KNN)
✅ Decision Tree Classifier
✅ Random Forest Classifier
✅ Naive Bayes Classifier
✅ Support Vector Machine (SVM)
✅ Confusion Matrix Explained
✅ Accuracy Score
✅ Precision
✅ Recall
✅ F1 Score
✅ ROC Curve & AUC
✅ Feature Selection
✅ Overfitting & Underfitting
✅ Bias-Variance Tradeoff
✅ Real-World Applications of Classification

Hands-on Practical:

🔹 Building Classification Models using Python
🔹 Working with Real Datasets
🔹 Data Preprocessing
🔹 Feature Engineering
🔹 Model Training & Testing
🔹 Performance Evaluation
🔹 Predicting Class Labels
🔹 Comparing Multiple Classification Algorithms

Who Can Join:

✔ Students and Freshers
✔ Developers and IT Professionals
✔ Cybersecurity Enthusiasts
✔ AI and Machine Learning Beginners
✔ Data Science Aspirants
✔ Anyone Looking to Build a Career in AI Engineering

About Techonquer:

Techonquer provides industry-focused training in Artificial Intelligence, Machine Learning, Cyber Security, Ethical Hacking, Cloud Security, SOC Operations, Digital Forensics, OSINT Investigation, Penetration Testing, and Emerging Technologies.

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