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Machine Learning + AI — Edspark Workshop
For Students, Freshers & Aspiring ML Engineers

Machine Learning
Workshop with GenAI

No advanced maths degree needed. Go from zero to building, evaluating, and deploying real ML models — with LLMs, Hugging Face, and modern AI tools woven into every module.

View Curriculum →
10
Modules
20+
Hours Live
14+
AI Tools
100%
Hands-On
Accredited by
🇮🇳 NSDCSkill IndiaGoogle for EducationIBMNSRIIM
— What You'll Gain —

Everything You Need to Build Real ML Systems

From regression to deep learning, NLP to deployment — master the complete ML workflow with GenAI tools at every step.

🧠

End-to-End ML Mastery

Regression, classification, clustering, NLP, time series, deep learning — every major ML paradigm in one intensive workshop.

🤖

GenAI Woven Into Every Topic

LangChain, Hugging Face, OpenAI API, and GitHub Copilot — use modern GenAI tools as part of your ML workflow from day one.

🚀

Real Model Deployment

Deploy ML models as live APIs using FastAPI and Streamlit. Build a portfolio with real deployed apps, not just Jupyter notebooks.

📜

Certification That Stands Out

Graduate with an Edspark + IBM verified certificate and deployed ML projects on GitHub that any recruiter can test live.

— Why ML + GenAI —

The Skill Stack Every AI Team Is Hiring For

📈
ML engineer roles are growing at 40% annually — and companies want engineers who can combine classical ML with modern LLMs and GenAI workflows.
⚡
GitHub Copilot writes boilerplate, Hugging Face provides pre-trained models, and LangChain connects everything. ML engineers who use GenAI tools are 8× more productive than those who don't.
🧠
By the end, you'll have deployed a real ML model as a live API, built an RAG pipeline, and used tools that appear on ML job descriptions at Google, Microsoft, and top AI startups.
🚀
Basic Python is all you need. We take you from preprocessing data all the way to deploying transformer models in production, step by step.
MACHINE LEARNING 📐Supervised 🔍Unsupervised 🧠Deep Learning 💬NLP / LLMs ML Engineer AI Engineer Data Scientist MLOps Engineer
— Curriculum —

10-Module Curriculum — Machine Learning with GenAI Built In

Every module teaches real ML skills + the exact AI tools professionals use for that skill in production systems.

M1 — Getting Started with ML
M2 — Data Preprocessing
M3 — EDA for ML
M4 — Regression Models
M5 — Advanced Classification
M6 — Clustering & Dimensionality
M7 — Neural Networks & Deep Learning
M8 — Time Series Forecasting
M9 — NLP Fundamentals
M10 — ML Capstone & Deployment
Module 1 — Getting Started with ML
Understand how machines learn — types of ML, the Scikit-learn ecosystem, and your first model in 30 minutes.
🤖 Core Topics
Supervised, Unsupervised, Reinforcement Learning
The ML pipeline: data → model → evaluate → deploy
Python setup: Scikit-learn, Pandas, Jupyter
Your first model: Linear Regression end-to-end
🤖 GenAI Integration NEW
ChatGPT to explain any ML concept with real-world analogies
GitHub Copilot to write your first Scikit-learn model from a description
Gemini in Colab for inline AI explanations while you code
Use AI to create a personalised ML learning plan and quiz set
TOOLS:ChatGPTGitHub CopilotGemini in ColabCodeium
Module 2 — Data Preprocessing
Clean and prepare data for ML — the step that determines 80% of your model's performance.
🧹 Core Topics
Handling missing data with imputation strategies
Feature scaling: normalisation and standardisation
Encoding categorical variables (LabelEncoder, OneHot)
Train/test split and cross-validation
🤖 GenAI Integration NEW
ChatGPT Code Interpreter — upload data, get full preprocessing recommendations
GitHub Copilot to generate complete Scikit-learn preprocessing pipelines
Use AI to detect the best imputation strategy for any dataset type
Julius AI for AI-powered data quality profiling in one click
TOOLS:ChatGPT CIGitHub CopilotJulius AI
Module 3 — EDA for ML
Explore data before modelling — find patterns, correlations, and the features that actually matter.
📊 Core Topics
Distribution analysis and outlier detection
Correlation matrices and feature importance
Matplotlib and Seaborn visualisation for ML
Feature selection techniques
🤖 GenAI Integration NEW
Julius AI — natural language EDA: "find the most important features" on any dataset
ChatGPT to explain what correlation patterns mean for your ML problem
GitHub Copilot to auto-complete Seaborn heatmaps and pairplot code
Use AI to generate feature engineering hypotheses from EDA findings
TOOLS:Julius AIChatGPTGitHub CopilotGemini in Colab
Module 4 — Regression Models
Predict continuous outcomes — from house prices to stock trends using linear and polynomial regression.
📐 Core Topics
Linear regression — theory and implementation
Polynomial and multiple regression
Regularisation: Ridge, Lasso, ElasticNet
Metrics: MAE, MSE, R² — interpreting results
🤖 GenAI Integration NEW
GitHub Copilot to generate full regression pipelines with evaluation code
ChatGPT to interpret regression coefficients in business language
Use AI to generate hyperparameter grids for Ridge/Lasso tuning
Weights & Biases for tracking regression experiments automatically
TOOLS:GitHub CopilotChatGPTW&B AICodeium
Module 5 — Advanced Classification
Classify data with high accuracy — Random Forest, SVM, XGBoost, and ensemble methods.
🎯 Core Topics
Logistic Regression and Decision Trees
Random Forest and Gradient Boosting
XGBoost and LightGBM
Hyperparameter optimisation with GridSearchCV
🤖 GenAI Integration NEW
ChatGPT to generate XGBoost parameter explanations and tuning strategies
GitHub Copilot for writing ensemble model stacking pipelines
Use AI to interpret SHAP feature importance plots in plain English
W&B Sweeps for automated hyperparameter search with AI suggestions
TOOLS:ChatGPTGitHub CopilotW&B AICodeium
Module 6 — Clustering & Dimensionality Reduction
Find structure in unlabelled data — customer segmentation, topic modelling, and PCA.
🔍 Core Topics
K-Means and DBSCAN clustering with real examples
Cluster evaluation: silhouette score, elbow method
PCA for dimensionality reduction
t-SNE for high-dimensional visualisation
🤖 GenAI Integration NEW
Julius AI for instant AI-powered customer segmentation from any CSV
ChatGPT to generate cluster labels and business descriptions from centroids
GitHub Copilot for t-SNE and UMAP visualisation code
Use AI to write segmentation strategy reports from clustering results
TOOLS:Julius AIChatGPTGitHub Copilot
Module 7 — Neural Networks & Deep Learning
How machines learn at scale — build and train neural networks with TensorFlow and Keras.
🧠 Core Topics
Perceptrons, activation functions, backpropagation
TensorFlow and Keras: build and train a network
CNNs for image recognition basics
Dropout, batch normalisation, regularisation
🤖 GenAI Integration NEW
GitHub Copilot to generate full Keras model architectures from descriptions
ChatGPT to explain loss curves and training behaviour in plain English
Hugging Face for accessing pre-trained CNN models instantly
Gemini in Colab for inline debugging help during model training
TOOLS:GitHub CopilotHugging FaceChatGPTGemini in Colab
Module 8 — Time Series Forecasting
Predict the future from historical data — ARIMA, LSTMs, and Prophet for business forecasting.
📅 Core Topics
Time series components: trend, seasonality, noise
ARIMA and SARIMA models
LSTMs for sequential prediction
Facebook Prophet for business forecasting
🤖 GenAI Integration NEW
ChatGPT to generate ARIMA/Prophet code from a business forecast requirement
GitHub Copilot for writing LSTM sequence input pipelines
Use AI to interpret time series patterns and write forecast reports
Julius AI for instant time series visualisation and trend detection
TOOLS:ChatGPTGitHub CopilotJulius AI
Module 9 — NLP Fundamentals
Teach machines to understand language — tokenisation, embeddings, sentiment analysis, and transformers.
💬 Core Topics
Tokenisation, stemming, lemmatisation
TF-IDF and word embeddings (Word2Vec)
Sentiment analysis and text classification
Intro to transformers and BERT
🤖 GenAI Integration NEW
Hugging Face pipeline — run sentiment analysis with 3 lines of code
OpenAI API for text embeddings and semantic similarity
LangChain basics — build a simple document Q&A system
ChatGPT to explain transformer attention mechanisms with diagrams
TOOLS:Hugging FaceOpenAI APILangChainChatGPT
Module 10 — ML Capstone & Deployment
Ship a real ML model as a live API — FastAPI, Streamlit, and cloud deployment end to end.
🚀 Core Topics
ML model evaluation and selection
FastAPI wrapper for ML model APIs
Streamlit for interactive ML dashboards
Deploy on Railway or Hugging Face Spaces
🤖 GenAI Integration NEW
GitHub Copilot to generate FastAPI endpoint code from your model
ChatGPT to write full project documentation and README
Use AI to generate LinkedIn project posts and portfolio writeups
Hugging Face Spaces for one-click free model deployment
TOOLS:GitHub CopilotChatGPTHugging Face SpacesW&B AI
— Hands-On Projects —

Build Real ML Projects

Graduate with live deployed ML models on GitHub and Hugging Face Spaces that any recruiter can test.

📦MODULE 5

XGBoost Fraud Detector

Train an XGBoost classifier on a financial transactions dataset. Use Copilot to write the pipeline, W&B to track experiments, and ChatGPT to write the report.

XGBoostCopilotW&B
💬MODULE 9

Sentiment Analysis API

Build a sentiment analyser using Hugging Face transformers. Wrap it in a FastAPI endpoint and deploy it live — use Copilot to generate all the deployment code.

Hugging FaceFastAPICopilot
🤖CAPSTONE

RAG-Powered Q&A System

Build a Retrieval-Augmented Generation (RAG) system using LangChain and OpenAI API. Upload documents, ask questions, get cited answers — deployed on Hugging Face.

LangChainOpenAI APIChromaDB
— AI Toolkit —

AI Tools You Will Learn & Use

14 real ML and GenAI tools across every module — building production-ready systems, not just notebooks.

Hugging Face
GitHub Copilot
ChatGPT
Julius AI
Gemini in Colab
W&B AI
LangChain
OpenAI API
Hugging Face
GitHub Copilot
ChatGPT
Gemini in Colab
LangChain
W&B AI
14+AI Tools
10Modules
100%Hands-On
0Prior Exp. Needed
— Career Outcomes —

Jobs You Can Get After This Workshop

Machine learning is the engine behind every AI product. Here are the roles this workshop directly prepares you for.

⚙️

ML Engineer

₹9 – 25 LPA

Build, train, and deploy ML models into production systems. Work at the intersection of data science and software engineering.

Scikit-learnTensorFlowFastAPI
Demand
Very High
🤖

AI Engineer

₹12 – 32 LPA

Build AI-powered products using LLMs, LangChain, and Hugging Face. The highest-paid and fastest-growing role in tech right now.

LLMsLangChainOpenAI API
Demand
🚀 Emerging
🔬

Data Scientist

₹7 – 22 LPA

Use ML to derive insights and predictions from business data. High demand at every company making data-driven decisions.

PythonML ModelsStatistics
Demand
Very High
🏭

MLOps Engineer

₹10 – 28 LPA

Keep ML models healthy in production — monitoring, retraining, and CI/CD pipelines for AI systems.

MLflowW&BDocker
Demand
🔥 Hot Role
💬

NLP Engineer

₹10 – 28 LPA

Build language-based AI systems — chatbots, document intelligence, and LLM-powered applications.

TransformersBERTLangChain
Demand
Very High
🧬

Research Engineer

₹12 – 35 LPA

Work on cutting-edge ML research at AI labs, universities, and R&D teams. Requires strong ML foundations.

PyTorchResearchPapers
Demand
High
🏢 Who's Hiring ML Professionals
GoogleMicrosoftAmazonOpenAIRazorpaySwiggyFlipkartTCSInfosysNVIDIA
3.1M+Global Job Openings
40%Annual Growth Rate
— Frequently Asked Questions —

Questions Students Usually Ask Before Joining

Here are the important details about the Machine Learning With GenAI workshop.

This is a 6-week workshop with live sessions, hands-on practice, assignments, and project-based learning.

No. This workshop starts from the basics, so students, freshers, and beginners can join comfortably.

Yes. GenAI tools are included in every module to help you understand AI-assisted threat analysis, report writing, policy creation, and security workflows.

The workshop is practical. You will work on real security concepts, tools, labs, case studies, and hands-on exercises.

Yes. After completing the workshop requirements, you will receive a completion certificate from Edspark.

Students, freshers, IT professionals, career switchers, and anyone interested in Machine Learning and GenAI can join.

Start building ML systems
that actually work — with AI.

Student, fresher, or developer — if you know basic Python, you can start this workshop today. We teach everything else.

⚡ Limited seats per cohort — freshers & students get priority!
  • Basic Python is all you need to start
  • Live sessions + real ML model labs every class
  • 1:1 mentorship from working ML engineers
  • GenAI tools integrated into every single module
  • Government-recognized certification + placement support

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