ML Club

### April Online Session: Feature Engineering for Machine Learning

💡 Want to boost your ML model’s performance? Feature Engineering is the key!
Feature Engineering is one of the most critical steps in building high-performing machine learning models. It involves transforming raw data into meaningful features that improve model accuracy and efficiency. In this hands-on session, we’ll cover the essential techniques and best practices for creating high-quality features.

### What You’ll Learn:

✅ Understanding the role of feature engineering in ML
✅ Handling missing data and outliers effectively
✅ Encoding categorical variables (One-Hot, Label Encoding)
✅ Scaling and normalizing numerical data
✅ Creating new features from existing ones
✅ Using feature selection techniques to improve models

### Who Should Attend?

📊 Data Scientists & ML Practitioners looking to improve model performance
🔍 Data Analysts who want to enhance their analytical skills
🚀 Anyone interested in machine learning and feature engineering
📌 Prerequisites: Basic knowledge of Python and machine learning concepts

📅 Date: 8th April, 2025
🕕 Time: 1800 London Time
🌍 Where: Online (Link provided upon registration)

🔗 Register Now & Take Your ML Skills to the Next Level!
#MachineLearning #FeatureEngineering #DataScience #MLClub #PythonML

### May Online Session: Web Scraping with Python (BeautifulSoup & Scrapy)

💡 Want to extract data from the web? Learn how to automate web scraping using Python!
The internet is a goldmine of valuable data, but how do you collect and structure it efficiently? In this hands-on session, we’ll explore web scraping techniques using BeautifulSoup and Scrapy, two powerful Python libraries that help extract data from websites.

### What You’ll Learn:

✅ Basics of web scraping and ethical considerations
✅ Extracting data using BeautifulSoup (HTML parsing)
✅ Using Scrapy for large-scale web scraping
✅ Handling pagination and dynamic content
✅ Exporting scraped data into structured formats (CSV, JSON, Pandas DataFrame)
✅ Avoiding common scraping pitfalls & anti-scraping mechanisms

### Who Should Attend?

🔍 Data Scientists & Analysts looking to automate data collection
💻 Developers interested in web scraping techniques
📊 Anyone working with data who wants to extract insights from online sources
📌 Prerequisites: Basic knowledge of Python

📅 Date: 13th May
🕕 Time: 6 PM
🌍 Where: Online (Link provided upon registration)

🔗 Register Now & Learn How to Scrape the Web Like a Pro!
#WebScraping #Python #DataScience #MLClub #BeautifulSoup #Scrapy

### June Online Session: Explainable AI & Model Interpretability (SHAP & LIME)

💡 Can you trust your machine learning models? Learn how to make AI explainable!
As machine learning models become more complex, understanding how they make decisions is critical—especially in high-stakes industries like finance, healthcare, and law. In this session, we’ll dive into Explainable AI (XAI) and explore tools like SHAP (SHapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) to interpret ML models.

### What You’ll Learn:

✅ Why model interpretability is crucial in machine learning
✅ Understanding black-box models vs. interpretable models
✅ How SHAP values help explain individual predictions
✅ Using LIME for model-agnostic explanations
✅ Best practices for making AI more transparent and ethical

### Who Should Attend?

📊 Data Scientists & ML Engineers who want to improve model transparency
🔍 Business & Domain Experts who need to understand ML decisions
🚀 Anyone working with AI models in real-world applications
📌 Prerequisites: Basic knowledge of machine learning

📅 Date: [Insert Date]
🕕 Time: [Insert Time]
🌍 Where: Online (Link provided upon registration)

🔗 Register Now & Learn How to Make AI Explainable!
#ExplainableAI #XAI #MachineLearning #DataScience #SHAP #LIME #MLClub

### July Online Session: Data Engineering with Apache Spark (PySpark Basics)

💡 Struggling with large datasets? Learn how to process big data efficiently with Apache Spark!

In today’s data-driven world, handling massive datasets efficiently is a must-have skill for data professionals. Apache Spark, powered by PySpark, is one of the most powerful tools for big data processing. In this hands-on session, we’ll introduce the fundamentals of PySpark and how it enables scalable and distributed data processing.

### What You’ll Learn:

✅ What is Apache Spark, and why is it useful?
✅ Understanding the Spark architecture & execution model
✅ Setting up PySpark and writing your first Spark application
✅ Data manipulation with Spark DataFrames
✅ Optimizing queries and performance tuning in PySpark

### Who Should Attend?

🚀 Data Engineers & Analysts working with large datasets
📊 Data Scientists who need scalable ML data pipelines
🔍 Anyone interested in distributed computing & big data
📌 Prerequisites: Basic knowledge of Python & SQL

📅 Date: 8th July 2025
🕕 Time: 6 PM
🌍 Where: Online (Link provided upon registration)

🔗 Register Now & Learn How to Scale Data Processing with PySpark!
#BigData #ApacheSpark #PySpark #DataEngineering #MLClub #DataScience