Mega Combo Offer: Full Stack Data Science with Generative AI, Agentic AI & Robotics

Mega Combo Offer: Full Stack Data Science with Generative AI, Agentic AI & Robotics

This comprehensive program equips learners with end-to-end skills in Data Science, Generative AI, Agentic AI, and Robotics. From Python programming, machine learning, deep learning, NLP, computer vision, and big data, to building intelligent agents, multi-modal AI systems, and autonomous robots, this course blends theory with hands-on projects. Students gain practical experience with tools like PyTorch, TensorFlow, LangChain, OpenAI API, Arduino, Raspberry Pi, Power BI, Tableau, and cloud platforms (AWS, GCP, Azure), preparing them for advanced AI roles and real-world problem-solving.

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1. Python & Programming Foundations

  • Python basics: Anaconda, IDEs, Identifiers, Comments, Keywords

  • Data types & structures: Strings, Lists, Tuples, Sets, Dictionaries

  • Operators & Control Flow: Loops, If-Else, Nested structures, Jump statements

  • Functions: Arguments, Recursion, Lambda, Built-ins

  • Modules & Exception Handling: Custom modules, Try-Except

  • File Handling: File types, File modes, OS module

  • OOP: Classes, Objects, Inheritance, Encapsulation, Polymorphism, Abstraction

  • Multithreading

2. Python Libraries for Data & Visualization

  • NumPy: Arrays, Operations, Functions

  • Pandas: Series, DataFrames, Cleaning & Transformation

  • Matplotlib & Seaborn: Charts, Graphs, Visualizations

  • Tkinter: GUI Apps

  • Multiple mini-projects

3. SQL & Databases

  • RDBMS vs NoSQL

  • Tables, CRUD, Functions, Joins, Subqueries

  • Grouping, Filtering, Constraints, Procedures, Set operations

4. Statistics for Data Science

  • Data collection, Sampling, Representation

  • Central Tendency, Dispersion, Skewness & Kurtosis

  • Correlation, Regression

  • Probability & Hypothesis Testing (Z-Test, T-Test, Chi-Square, ANOVA, F-Test)

  • Linear Regression

5. Advanced Excel

  • Data Formatting & Transformation

  • Functions: Math, Logical, Lookup

  • Pivot Tables & Charts, Conditional Formatting

  • Flash Fill, Macros, Worksheet Protection

6. Power BI

  • Power BI Desktop vs Service

  • Data Import, Transformation & Modeling

  • DAX: Columns, Measures, Variables, Functions

  • Visualizations: Charts, Maps, KPIs, Treemaps, Funnel

  • Filters, Slicers, Drill Downs

  • Dashboards, Q&A Visuals, Smart Narratives

  • Optimization & Best Practices

7. Machine Learning

  • AI vs ML vs DL vs Data Science

  • Data Preprocessing, EDA, Visualization

  • Supervised: Regression, Classification (Logistic, KNN, SVM, Trees, Random Forest, Naive Bayes)

  • Model Evaluation: Confusion Matrix, Precision, Recall, F1, ROC-AUC

  • Unsupervised: Clustering, PCA

  • Advanced: Ensemble, Boosting, Hyperparameter Tuning

  • Time Series Forecasting

  • NLP Basics, Ethics & Bias

  • Real-world ML Projects

8. Big Data

  • Hadoop Ecosystem: HDFS, YARN, Hive, Pig, HBase

  • Data Transfer: Sqoop, Flume

  • Apache Spark: RDD, DataFrames, SQL, PySpark, Streaming

  • Big Data on Cloud: AWS, Azure, GCP

9. Cloud Computing

  • Service Models: IaaS, PaaS, SaaS

  • Deployment Models: Public, Private, Hybrid

  • Virtualization & Hypervisors

  • Cloud Services: EC2, S3, IAM, Networking

  • Serverless: AWS Lambda, GCP Functions

  • Cloud Security & Monitoring

  • Deploying Web & AI Apps

10. Tableau

  • Tableau Interface & Data Sources

  • Data Cleaning & Preparation

  • Charts: Bar, Line, Pie, Maps, TreeMaps

  • LOD Expressions, Parameters, Calculated Fields

  • Dashboards & Story Points

  • Publishing (Public, Server)

11. Deep Learning

  • Foundations: Math, Optimization

  • Frameworks: TensorFlow, Keras, PyTorch

  • ANN, CNN (ResNet, VGG, Inception), RNN, LSTM, GRU

  • Transfer Learning

  • Projects: Image & Text

12. NLP (Natural Language Processing)

  • Text preprocessing, Tokenization

  • Feature Extraction: BoW, TF-IDF, Word2Vec, GloVe, FastText

  • Text Classification: Sentiment, Spam, NER, POS

  • Sequence Models: LSTM, GRU, Attention, Seq2Seq

  • Advanced NLP: Summarization, Translation, QA

  • Deployment: Flask, Streamlit, Cloud

13. Computer Vision

  • Image basics: Pixels, Channels

  • Image Processing: Thresholding, Blurring, Edge Detection

  • CNNs, Transfer Learning, Data Augmentation

  • Object Detection: R-CNN, YOLO, SSD

  • Image Segmentation: U-Net, Mask R-CNN

  • Applications: Face/Gesture Recognition, OCR, Video Processing

  • Deployment: Web, Mobile, Cloud

14. Generative AI

  • Foundations, Key Concepts & Applications

  • Models: GANs, VAEs, Diffusion, LLMs

  • Tools: PyTorch, TensorFlow, Hugging Face, LangChain

  • GANs: DCGAN, CycleGAN, StyleGAN, Applications (Deepfakes, Art, Style Transfer)

  • VAEs: Encoder-Decoder, Latent Space, Anomaly Detection, Compression

  • LLMs: Transformers, Fine-tuning, RAG with Vector DBs (Pinecone, Weaviate)

  • Diffusion: Stable Diffusion, ControlNet

  • Multi-Modal AI: CLIP, ALIGN (Text+Image+Audio)

  • Training & Optimization: LoRA, QLoRA, PEFT

  • Deployment: Flask, FastAPI, Gradio, LangChain Agents

  • Ethics: Bias, Privacy, Copyright

15. Agentic AI

  • Foundations: Agentic AI vs Generative AI

  • Agent Types: Reactive, Proactive, Collaborative

  • LLM Architecture: Tokens, Embeddings, Transformers

  • Environment Setup: Python, OpenAI API

  • Conversational Pipelines & RAG: Prompt engineering, vector DBs (FAISS, Pinecone)

  • LangChain: Chains, Memory, Tools, Agents, Output Parsers

  • LangGraph: Multi-step workflows, State management, Error handling

  • Multi-Agent Systems: CrewAI, AutoGen, Role-based delegation

  • Deployment: FastAPI, Streamlit, Cloud (AWS/GCP/Azure), Monitoring (LangFuse, Portkey)

  • Security, Ethics, Trust: TRiSM, Bias, Hallucination, Privacy

  • Capstone: Legal researcher, CRM assistant, AI tutor, Self-evolving agents, Multimodal agentic AI

16. Robotics with AI

  • Robotics Overview: Industrial, Service, Humanoid, Swarm, Autonomous Vehicles

  • Tools: Arduino, Raspberry Pi, TinkerCAD, Proteus

  • Hardware & Sensors: Breadboards, Resistors, LEDs, Motors, Ultrasonic, IR, Temp, Humidity, Accelerometer, Gyroscope

  • Actuators & Motor Drivers: DC, Stepper, Servo, L298N, L293D

  • Arduino IDE: Loops, I/O, Projects (LED Blink, RGB, Traffic Light)

  • Raspberry Pi: OS Setup, Linux, GPIO, Sensor Integration, Smart Home Automation

  • IoT Integration: Bluetooth, Wi-Fi, Cloud (ThingSpeak, Blynk)

  • Motion Control: PWM, Robotic Arm, DC Motor Controller

  • AI in Robotics: OpenCV, Object/Gesture Recognition, Face/Person Recognition

  • ML/DL Integration: Autonomous decision-making

  • Projects: Face Detection Security, Gesture-Controlled Devices, AI-Powered Autonomous Robot

17. Capstone Project

  • End-to-end project integrating Data Science + Generative AI + Agentic AI + Robotics

  • Examples:

    • AI-powered Smart Surveillance Robot

    • Multi-modal GenAI Healthcare Assistant Robot

    • IoT + AI Smart City Automation

    • Conversational Agent-based Productivity Assistant

Course Outcomes

ChatGPT said:

Here’s a concise, mixed job + knowledge-oriented course outcome for your mega combo course:

Course Outcomes:

  1. Full-Stack Data Science Expertise:

    • Master Python, SQL, statistics, Excel, Power BI, Tableau, and big data tools.

    • Apply ML, DL, NLP, and computer vision techniques to solve real-world problems.

  2. Generative AI & Agentic AI Proficiency:

    • Design, train, fine-tune, and deploy GANs, VAEs, LLMs, diffusion models, and multi-modal AI systems.

    • Build intelligent agents using LangChain, RAG, LangGraph, and multi-agent frameworks.

    • Implement autonomous workflows, prompt engineering, and AI agent decision-making.

  3. Robotics & AI Integration:

    • Develop robotic systems with Arduino, Raspberry Pi, and IoT integration.

    • Apply AI/ML for computer vision, gesture recognition, autonomous decision-making, and smart automation.

  4. Deployment & Cloud Skills:

    • Deploy AI, ML, and robotic solutions on cloud platforms (AWS, Azure, GCP).

    • Build serverless, scalable, and secure AI applications and agents.

  5. Job-Ready & Project-Based Learning:

    • Hands-on projects, capstone assignments, and real-world simulations for roles like AI Engineer, Data Scientist, Robotics Engineer, and LLMOps Specialist.

    • Strong portfolio demonstrating integrated skills across data science, AI, and robotics.

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