ML Club

### 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

### August Online Session: End-to-End ML Model Deployment (FastAPI & Streamlit)

💡 You've built a great ML model—now what? Learn how to deploy it for real-world use!
Building an ML model is just the first step. To make an impact, you need to deploy it so others can use it! In this hands-on session, we’ll explore FastAPI and Streamlit, two powerful Python frameworks for deploying machine learning models as APIs and interactive web applications.

### What You’ll Learn:

✅ Setting up an ML model API using FastAPI
✅ Deploying an interactive ML app with Streamlit
✅ Handling user inputs and making real-time predictions
✅ Connecting frontend interfaces with ML models
✅ Best practices for model deployment and scaling

### Who Should Attend?

📊 Data Scientists & ML Engineers who want to deploy models in production
💻 Developers looking to integrate ML into web applications
🚀 Anyone interested in MLOps and real-world AI applications
📌 Prerequisites: Basic Python & machine learning knowledge

📅 Date: 12 August 2025
🕕 Time: 1800 London
🌍 Where: Online (Link provided upon registration)

🔗 Register Now & Learn How to Deploy Your ML Models Like a Pro!
#MachineLearning #ModelDeployment #FastAPI #Streamlit #MLOps #MLClub #DataScience