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Core Objectives

• Transition from Data to Decisions: Build predictive and prescriptive models.
• Master Modern AI: Hands-on with LLMs, Reinforcement Learning & Neural Networks.
• Industry Readiness: Deploy production-level AI applications.

🛠️ What You Will Master

• Advanced Python, SQL (Big Data), Git Version Control.
• Hypothesis Testing, Probability & Optimization.
• Machine Learning: Regression, XGBoost, Clustering, PCA.
• Deep Learning: Transformers, BERT, Computer Vision.
• Generative AI: Prompt Engineering & Llama 3 fine-tuning.
• MLOps: Docker, Kubernetes & Cloud Deployment (AWS/Azure).

⏱️ Timeline & Effort

• Duration: 7–9 Months
• Time Commitment: 8–12 Hours per Week
• Format: Live Online + Self-paced + Weekend Workshops

📘 Phase 1: Foundation (Months 1–2)

• Linear Algebra, Calculus & Inferential Statistics.
• Python (Pandas, NumPy, Matplotlib).
• Advanced SQL (Joins, Window Functions).
• Data Wrangling & Feature Engineering.

🤖 Phase 2: Machine Learning (Months 3–4)

• Supervised Learning: Regression, Trees, Random Forest.
• Unsupervised Learning: K-Means, PCA.
• Performance Metrics: Bias-Variance, F1-score, Cross-validation.

🚀 Phase 3: Deep Learning & GenAI (Months 5–6)

• Neural Networks & Backpropagation.
• CNNs for Vision & Transformers (BERT/GPT).
• RAG, Vector DB (Pinecone/Milvus), LangChain AI Agents.

☁️ Phase 4: MLOps & Deployment (Month 7)

• Git Versioning, Docker Containerization, Kubernetes Orchestration.
• Cloud APIs via AWS Lambda or Azure.
• Capstone Project: Industry-grade AI application.

💎 Key Benefits

• High resume visibility & MNC screening advantage.
• Dual Alumni Status & Job Portal Access.
• Weekly Mentorship from industry Data Scientists.
• Average 40%–70% Salary Hike.

📅 Timeline Summary