Advanced Full Stack Data Science, AI & ML
The Full Stack Data Science, AI & ML program is a job-ready bootcamp designed to cover the complete AI lifecycle—from data collection, data wrangling, and analysis to machine learning, deep learning, MLOps, and AI deployment. Learners will master Python, SQL, and Big Data tools, build and optimize Machine Learning and Deep Learning models, explore AI, LLMs, Generative AI, and NLP, and gain practical expertise in MLOps for building and scaling production-ready ML systems. The program also includes real-world industry projects to help learners apply their skills to practical business problems. By the end of the program, participants will be prepared for roles such as Data Scientist, Machine Learning Engineer, AI Developer, and MLOps Engineer.
Course Curriculum
Module 1: Foundations of Data Science & AI
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Introduction to Data Science, AI & ML
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Python Programming for AI (NumPy, Pandas, Matplotlib, Seaborn)
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Statistics & Probability for Machine Learning
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Linear Algebra & Calculus essentials for ML
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Hands-on: Exploratory Data Analysis (EDA) with Python
Module 2: Databases, Data Engineering & Big Data
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SQL (MySQL, PostgreSQL) for Data Science
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NoSQL (MongoDB, Cassandra) for unstructured data
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Data Pipelines (ETL/ELT with Airflow & Spark)
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Data Warehousing & Streaming (Kafka basics)
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Hands-on: Build a mini ETL pipeline
Module 3: Machine Learning (Core AI)
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Supervised Learning: Regression, Classification
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Unsupervised Learning: Clustering, Dimensionality Reduction
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Ensemble Methods: Random Forest, XGBoost, CatBoost
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Model Evaluation, Cross-Validation, Hyperparameter Tuning
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Hands-on: Predictive Modeling on Real Dataset
Module 4: Deep Learning & Generative AI
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Neural Networks (ANN fundamentals)
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CNNs for Image Processing
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RNNs, LSTMs, GRUs for Time Series & NLP
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Transformers & LLMs (BERT, GPT-style models)
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Generative AI: Text, Image & Audio synthesis
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Hands-on:
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Image Classifier with CNNs
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Text Generator with Transformers
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Module 5: AI Deployment & MLOps
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Model Serving with Flask/FastAPI
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Docker & Kubernetes for ML Models
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MLflow & DVC for Experiment Tracking & Versioning
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CI/CD pipelines for ML (Jenkins, GitHub Actions)
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Cloud ML Platforms: AWS Sagemaker, Azure ML, GCP Vertex AI
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Hands-on: Deploy ML model on Cloud
Module 6: Capstone Project & Career Path
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Industry-specific Capstone Projects (Finance, Retail, Healthcare, Cybersecurity, NLP, CV)
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Building a Data Science & AI Portfolio (GitHub, Kaggle)
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Resume Building, Mock Interviews & Career Prep
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Roadmap to Certifications: TensorFlow Developer, AWS ML Specialty, Azure AI Engineer, Databricks ML
Take the First Step Towards Smarter Learning.
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