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

### September Online Session: LLMOps with Optik – Managing Large Language Models in Production

💡 Building powerful LLMs is one thing—operationalizing them is another! Learn how to deploy and manage Large Language Models efficiently.

With the rise of Large Language Models (LLMs), organizations face new challenges in deployment, monitoring, and optimization. In this hands-on session, we’ll explore LLMOps—the best practices for deploying, managing, and maintaining LLMs in production using Optik.

### What You’ll Learn:

✅ What is LLMOps, and why does it matter?
✅ Introduction to Optik for LLM model optimization
✅ Deploying LLMs efficiently in cloud & edge environments
✅ Monitoring model performance & ensuring reliability
✅ Handling real-world challenges like drift, latency & scaling

### Who Should Attend?

🚀 ML Engineers & Data Scientists working with LLMs
🔍 MLOps & DevOps Professionals interested in LLM deployment
📊 AI Enthusiasts & Researchers exploring LLM applications
📌 Prerequisites: Basic knowledge of machine learning & deep learning

📅 Date: 9th September 2025
🕕 Time: 1800 London
🌍 Where: Online (Link provided upon registration)

🔗 Register Now & Master LLMOps for Scalable AI Solutions!
#LLMOps #Optik #LargeLanguageModels #MachineLearning #MLOps #MLClub #AI