Rebioin AI Rebioin AI Build AI Skill for tomorrow's leader
Explore Courses
Computational Sciences 17% OFF

NEXT-GEN AI & DATA INTELLIGENCE PROGRAM

to: 1. Develop Generative AI applications using LLMs, prompt engineering, RAG and AI agents. 2. Apply Explainable AI techniques using SHAP, LIME and counterfactual analysis. 3. Build advanced forecasting models using statistical, deep learning and Transformer architectures. 4. Work with graph data, knowledge graphs and Graph Neural Networks. 5. Implement MLOps workflows, model versioning, containerization and deployment. 6. Develop interactive AI dashboards and edge AI applications. 7. Understand Federated Learning, Causal AI and privacy-preserving machine learning. 8. Build scalable data engineering pipelines using Spark, Kafka and Airflow. 9. Apply multimodal AI and vision-language models to scientific and business applications. 10. Develop end-to-end AI solutions and professional project portfolios.

All Levels 12 Weeks 0 Lessons 0 Learners English
0Learning items
12 WeeksCourse duration
0Active learners
80%Completion rule
COURSE OVERVIEW

About this course

Everything you need to understand the course before you begin.

Week 01: Generative AI & Large Language Models

  1. Day 01: Generative AI landscape – LLMs, tokenization, Transformer architecture and real-world applications.
  2. Day 02: Prompt engineering – zero-shot, few-shot and chain-of-thought prompting.
  3. Day 03: Advanced prompting – function calling, JSON mode, structured outputs and guardrails.
  4. Day 04: Embeddings and vector databases – FAISS, ChromaDB and Pinecone.
  5. Day 05: Hands-on – build a basic semantic search application using text embeddings and a vector database.

Week 02: RAG, Fine-Tuning & AI Agents

  1. Day 06: Retrieval-Augmented Generation (RAG) architecture – LangChain, document loaders and retrievers.
  2. Day 07: Practical RAG – build a chatbot that interacts with PDF and CSV files.
  3. Day 08: LLM fine-tuning – LoRA, QLoRA and parameter-efficient fine-tuning concepts.
  4. Day 09: AI agents – tools, memory, ReAct architecture and synthetic data generation.
  5. Day 10: Project – develop and deploy an AI-powered data analysis assistant using Streamlit.

Week 03: Explainable AI (XAI) & Responsible AI

  1. Day 11: Introduction to Explainable AI – model interpretability, bias, fairness and responsible AI.
  2. Day 12: SHAP – global and local model explanations.
  3. Day 13: LIME, permutation importance and feature importance analysis.
  4. Day 14: Counterfactual explanations and what-if analysis.
  5. Day 15: Project – explain a credit-risk or disease-prediction model using a SHAP-based interactive dashboard.

Week 04: Advanced Time-Series Forecasting

  1. Day 16: Time-series fundamentals – EDA, stationarity, trend, seasonality, ARIMA and SARIMA.
  2. Day 17: Deep learning for time series – LSTM and GRU architectures.
  3. Day 18: Business forecasting using Facebook Prophet.
  4. Day 19: Transformer-based forecasting – PatchTST and Temporal Fusion Transformer (TFT).
  5. Day 20: Project – develop a TFT-based forecasting model for crop yield or disease trends.

Week 05: Graph Data Science & AutoML

  1. Day 21: Graph theory, NetworkX, graph representations and knowledge graphs.
  2. Day 22: Graph Neural Networks – GCN and GAT using PyTorch Geometric.
  3. Day 23: Graph-based applications – drug discovery, molecular interaction networks and social network analysis.
  4. Day 24: Automated Machine Learning – H2O, PyCaret and AutoGluon.
  5. Day 25: Project – develop a GNN-based drug-target interaction prediction model and apply AutoML to business data.

Week 06: MLOps & Model Lifecycle Management

  1. Day 26: MLOps fundamentals and MLflow – experiment tracking and model registry.
  2. Day 27: Data and model versioning using DVC.
  3. Day 28: Docker fundamentals – containerization and ML model packaging.
  4. Day 29: Kubernetes basics, CI/CD pipelines and GitHub Actions.
  5. Day 30: Project – deploy and monitor a machine learning model using MLflow and Docker.

Week 07: AI Visualization, Business Intelligence & Edge AI

  1. Day 31: Interactive dashboards using Streamlit and Plotly.
  2. Day 32: AI-powered business intelligence – Power BI Copilot and Tableau AI.
  3. Day 33: Natural language to dashboard – generating visualizations and analytical reports using LLMs.
  4. Day 34: Edge AI fundamentals – TinyML, model quantization, Raspberry Pi and NVIDIA Jetson.
  5. Day 35: Project – build a smart agriculture IoT application with real-time data analytics.

Week 08: Federated Learning & Causal AI

  1. Day 36: Federated Learning fundamentals – FedAvg and privacy-preserving machine learning.
  2. Day 37: Federated Learning in healthcare – cross-silo training and collaborative medical AI.
  3. Day 38: Federated Learning in banking – privacy-aware fraud detection.
  4. Day 39: Causal AI – A/B testing, causal graphs and DoWhy framework.
  5. Day 40: Project – develop a causal uplift prediction model for business optimization.

Week 09: Data-Centric AI & Synthetic Data Generation

  1. Day 41: Data quality assessment, data drift, data labeling and validation strategies.
  2. Day 42: Feature engineering, feature selection and active learning.
  3. Day 43: Weak supervision and automated labeling using Snorkel.
  4. Day 44: Synthetic data generation using GANs and diffusion models.
  5. Day 45: Project – generate and evaluate a privacy-conscious synthetic healthcare dataset.

Week 10: Data Engineering for AI

  1. Day 46: Modern data architecture – data lakes, data warehouses and lakehouse architecture.
  2. Day 47: Apache Spark and PySpark – distributed data processing and ETL pipelines.
  3. Day 48: Apache Kafka – real-time data streaming and event-driven architectures.
  4. Day 49: Apache Airflow – DAGs, workflow orchestration and pipeline scheduling.
  5. Day 50: Project – develop an end-to-end data engineering pipeline using Snowflake and Databricks for machine learning.

Week 11: Multimodal AI & Vision-Language Models

  1. Day 51: Introduction to multimodal AI – integrating text, images and video.
  2. Day 52: Vision-language models – CLIP, BLIP and cross-modal embeddings.
  3. Day 53: Text and video understanding – multimodal information extraction and summarization.
  4. Day 54: Biomedical image analysis using vision-language models.
  5. Day 55: Project – develop an X-ray image-to-report generation prototype using a vision-language model.

Week 12: AI in Business Intelligence & Final Capstone

  1. Day 56: Conversational analytics using LLMs and natural language querying.
  2. Day 57: AI-powered dashboards, automated insights and business reporting.
  3. Day 58: Predictive analytics, automated report generation and decision-support systems.
  4. Day 59: Final capstone development – integrate at least three program topics into one end-to-end application.
  5. Day 60: Final project demonstration, technical presentation, portfolio review and future career roadmap.



COURSE CURRICULUM

What you will learn

Explore the complete learning path, section by section.

0Sections 0Lessons
Curriculum is being preparedPublished lessons will appear here soon.