Domain Specific Training in Applied Artificial Intelligence & Data Science
Durations – 300 Hrs.-Online & Offline
Program Objective
- This program aims to develop industry-ready professionals with strong skills in programming, data analysis, machine learning, deep learning, and generative AI. Through hands-on projects and real-world tools, students will gain the ability to build intelligent, data-driven solutions across various domains.
Program Structure
Semester 3: Core Programming and Data Management
- Relational Databases (RDBMS)
- Python Programming
Semester 4: Data Analysis
- Excel for Data Analysis with Pandas
Semester 5: Machine Learning
- Machine Learning Fundamentals & Advanced ML
- ML System Design & Deployment (ML Ops)
Semester 6: Advance AI
- Deep Learning using Tensor Flow
Semester 7: NLP and Generative AI
- NLP and Generative AI & Agentic AI
- Experiential Project Based Learning
Program Outcome
- Demonstrate proficiency in Python programming, data handling, and database management
- Analyze and visualize complex datasets using Excel, Power BI, and Python-based tools
- Apply supervised and unsupervised machine learning techniques to solve real-world problems
- Design, train, and deploy deep learning models for image, text, and sequence data
- Develop natural language processing and generative AI applications using modern AI frameworks
Project stream
Core Programming and Data Visualization
- Student Grading System Using Python Functions
- Automated Sales Performance Dashboard in Excel
- Customer Segmentation with Grouped Insights
Machine Learning and Deep Learning
- Credit Risk Analysis Using Ensemble
- Multi-Class Image Classification with CNN
- License Plate Detection with OpenCV
Software & Frame work required
- Python & Jupyter Notebook
- MySQL
- MS Excel
- Anaconda / VS Code
- TensorFlow, Keras, PyTorch, OpenCV
- Streamlit, Gradio, Hugging Face
Semester 3: Core Programming and Data Management
RDBMS using MySQL – 40 hrs. – 7 days – 1 week ( Online )
| Introduction to databases and RDBMS, | Database creation, concept of relation and working examples | Creating tables. Design view of the table, Alter table operations & Key Constraints |
| Read, update and delete operations on tables. Working with nulls | Querying tables: Select statement, examples and its variations | Filtering, Sorting, Predicates and working examples |
| Joins in SQL and working examples | Insert, Update, delete operations and working examples | Scalar functions in SQL and working examples |
| SQL set based operations and data aggregation Sub-queries in SQL |
Normalization and de-normalization: Views and Temporary tables Transactions in SQL |
SQL programming Creating stored procedures, Cursors in SQL |
| Assessment – Module Test – MCQ, Theory | Technical Mock |
Python Programming & Advanced Python – 72 hrs. – 12 days – 2 weeks
| Introduction to Python | Python Data types and Conditions | Control Statements |
| Python Functions | Default arguments | Functions with variable number of args |
| Scope of Variables | Global specifier | Working with multiple files |
| List and Tuple | List Methods | List Comprehension |
| Map and filter functions | String | List comprehension with conditionals |
| Set and dictionary | Exception Handling | File Handling |
| Object Oriented Programming | Overloading Operator | Inheritance |
| Regular Expression | Finding Patterns of Text | Meta characters |
| Testing Fundamentals | Unit Testing | Working with JSON |
| Decorators | Iterators | Generators |
Problem Solving and Data Structures using Python — 18 hrs. — 3 Days — 0.5 weeks
| Time and Space Complexity | Utopian Tree | Viral Advertising |
| Birthday Cake Candles | Migratory Birds | Kaprekar Number |
| Pangram String and Anagram String | Palindrome Index | Array Rotation |
| Project - Intermediate project & Demo | Assessment — Module Test — MCQ, Theory | Technical Mock |
Semester 4: Data Analysis
Exploratory Data Analysis with Pandas — 36 hrs. — 6 Days — 1 weeks
| NumPy | Vectorization | Broadcasting |
| Slicing of Matrices | Filtering | Array Creation Functions |
| NumPy Functions across axis | Stacking of arrays | Matrix Calculation |
| Pandas Series | Data Cleaning | Handling Missing Data |
| Pandas Data frame | Selection Data (loc, iloc) | Filtering Data Frames |
| Working with Categorical Data | Grouping & Aggregation | Merging Data Frame (concat, merge) |
| Sorting Data Frames | Importing csv files | Importing Excel Files |
| Creating graphs using Matplotlib | Customizing Plots | Seaborn, PlotLy |
| Project - Advanced project & Demo | Assessment — Module Test — MCQ, Theory | Technical Mock |
Semester 5: Machine Learning
Machine Learning Fundamentals & Advanced ML — 60 hrs. — 10 days — 1.5 weeks
| Introduction to Machine Learning | Regression | Logistic regression |
| Supervised machine learning | Simple linear regression | Naïve Bayes Classification |
| Unsupervised machine learning | Multiple linear regression | Decision trees and its types |
| Train test split the data | Performance measure for regression | K Nearest Neighbor Classification |
| ML Workflow for project implementation | Classification and types | Performance Measure for Classification |
| Random Forest, | Clustering and types | Evaluate clustering results, Elbow Plot |
| Optimizing regression models with forward elimination, grid search cv | Improving classification models with Ensemble modeling | Model evaluation strategies (KFold, Stratified KFold) |
| Regularization L1 and L2 regularization | Bagging | Boosting techniques: ADA boost |
| Hyperparameter Tuning, SVM | Stacking and Voting | Dimensionality Reduction with PCA |
| Project - Advanced project & Demo | Assessment — Module Test — MCQ, Theory | Technical Mock |
ML System Design & Deployment (MLOps) - 30 hrs - 5 Days - 1 weeks ( online )
| MLOps Fundamentals | Reproducible Project Setup | Data Versioning & Validation |
| Experiment Tracking & Model Registries | Feature Store Fundamentals | Automating with CI/CD Pipelines |
| Model Deployment Patterns | Advanced Deployment Strategies | Production Model Monitoring |
| ML Infrastructure & Scaling | ML Pipeline Orchestration | End-to-End MLOps Project |
| Assessment — Module Test — MCQ, Theory | Technical Mock |
Semester 6: Advance AI
Deep Learning using Tensor Flow - 36 hrs. - 6 Days - 1 weeks
| What is Deep Learning | Performance measure for ANN | Building project based on CNN |
| Deep Learning Methods | Need for Hardware's in Deep Learning | Need for Data augmentation |
| Deep Learning Application | Basics of image processing | Batch Normalization, dropout |
| Artificial Neural Network | Opencv library | Object detection with CNN |
| hidden Layers | Image reading, writing, enhancement | Object recognition with CNN |
| Activation Function | Edge detection, filtering, morphology | Forward and Backward propagation |
| CNN for computer vision | CNN architecture and its types | Tensorflow, PyTorch, Keras |
| Project- Advanced project & Demo | Assessment – Module Test – MCQ, Theory | Technical Mock |
| Recurrent Neural Network (RNN) | Long- Short term Memory (LSTM) | Basic Open CV Functions |
| Semester 7: NLP and Generative AI & Agentic AI | ||
| Natural Language Processing – 36 hrs. 6 Days - 1 weeks | ||
| Introduction to NLP | NLP: Areas of Application | Understanding the Text |
| Text Encoding | Word frequencies and stop words | Bag of words representation |
| Stemming and Lemmatization | TF- IDF representation | Canonicalisation |
| Phonetic Hashing | Spell Corrector | Point wise mutual Information |
| Gensim, Word2Vec | Word Embeddings | Named Entity Recognition (NER) and Parts of Speech Tagging |
| Dependency Parsing and Syntactic Analysis | Semantic Similarity and Sentence Embeddings | Bidirectional LSTM |
| Project- Advanced project & Demo | Assessment – Module Test – MCQ, Theory | Technical Mock |
| Generative AI & Agentic AI - 36 hrs - 6 Days - 1 weeks | ||
| Introduction to Gen AI | Rule-based vs neural generation | Generative Adversarial Network |
| Variable Auto Encoder | Transformers | Application of Generative AI, Ethics |
| FastText and subword models | Sentence embeddings and similarity | Encoding long text documents |
| Visualizing embeddings with tools | Prompt Engineering | Zero shot and few shot prompts |
| Chain-of-thought prompting style | System and user prompts | Common prompt engineering mistakes |
| Agentic AI | Application of Agentic AI | Developing an AI Agent |
| Project- Application Development | Assessment – Module Test – MCQ, Theory | Technical Mock |
| Experiential Project based Learning | ||
| An end-to-end Deep learning model development using scikit-learn, Tensor Flow and real-world datasets with model deployment. (DAV+ML+DL+NLP) | ||
