Build Your First Email Spam Classifier – A Practical ML
From Inbox to Insight: Learn ML by Fighting Spam
Skills you will gain:
About Program:
Build Your First Email Spam Classifier – A Practical ML is a beginner-friendly international workshop that offers a perfect gateway into the world of machine learning. Through the creation of an end-to-end spam detection system, participants will learn essential ML concepts including data preprocessing, feature extraction, model training, evaluation, and performance tuning.
With a strong emphasis on hands-on coding and practical implementation, learners will walk away with both knowledge and a portfolio-ready project.
Aim: To introduce participants to machine learning (ML) through a practical, real-world application: building an email spam classifier using Python. The workshop aims to cover the complete ML pipeline from data preparation to model deployment in an easy-to-understand, hands-on format.
Program Objectives:
- Teach practical machine learning using a hands-on project
- Make learners comfortable with tools like scikit-learn, Pandas, and NLTK
- Introduce basic ML models suitable for NLP tasks
- Empower participants to apply their skills in other text classification use cases
What you will learn?
Day 1: Understanding the Problem and Preparing Your Data
● ML Basics & Spam Filtering Relevance
● Setting Up Google Colab
● Exploring & Cleaning Spam Dataset
Day 2: Building and Training the Decision Tree Model
● Feature Engineering
● Training a Decision Tree Classifier
● Intro to Model Evaluation
Day 3: Evaluating, Optimizing & Deploying Your Model
● Confusion Matrix & Hyperparameter Tuning
● Visualizing Decision Tree
● Real-world Use & Deployment Ideas
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- Students (UG/PG) from any STEM background
- Beginners in data science or machine learning
- Software developers seeking hands-on AI/ML experience
- Educators and academic researchers
- Anyone interested in practical applications of ML with Python
Career Supporting Skills
Program Outcomes
- Understand the complete ML development lifecycle
- Learn text preprocessing and feature engineering techniques
- Build and evaluate a working spam classifier
- Gain confidence to work on classification problems using real data
- Receive a recognized certification and reusable code templates