Applied Data Science with AI/ML &Gen AI
Duration – 15 Days.
(Fast – Track)
Program Structure
- Python Programming – Prerequisite
- Data Analysis using Pandas – Prerequisite
-
Machine Learning – 3 Days
- Supervised & Unsupervised Learning
- Regression
- Classification
- Model Improvement
-
Deep Learning – 5 Days
- TensorFlow
- ANN
- CNN
- OpenCV
-
Natural Language Processing – 5 Days
- Tokenization
- Named Entity Recognition (NER)
- NLTK
-
Gen AI & Agentic AI – 2 Days
- Transformers
- RAG
- Prompt Engineering
Project Stream
- Machine Learning and Deep Learning Deploy using Streamlit or Hugging Face
Program Outcomes
- Build and evaluate Machine Learning models for regression, classification, and clustering to solve real-world business problems.
- Develop and deploy Deep Learning models using CNNs and frameworks like TensorFlow, PyTorch, and Keras.
- Apply Natural Language Processing techniques including tokenization, stemming, embeddings, and text classification using ML and RNNs.
- Understand and implement Generative AI concepts such as GANs, VAEs, Transformers, and Prompt Engineering.
Experiential Project Based Learning
- End-to-End AI Project Experience
Tools / Platform:
- Google Colab / Jupyter Notebook
- OpenAI, Gemini, Copilot, Hugging Face, LangChain
Assessment – MCQ’s , Module Test
| Machine Learning | ||
|---|---|---|
| Understand what is Machine Learning | Regression | Logistic regression |
| Supervised machine learning | Simple linear regression | Naïve Bayed Classification |
| Unsupervised machine learning | Multiple linear regression | Decision tress and its types |
| Train test split the data | Performance measure for regression | K Nearest Neighbor Classification |
| ML Workflow for project implementation | MSE, R-Squared, MAE, SSE | Performance Measure for Classification |
| Classification | Various types of classification | Accuracy, Recall, Precision, F1measure |
| Sorting Data Frames | Importing csv files | Importing Excel Files |
| Deep Learning | ||
| What is Deep Learning | Hidden Layers | Building project based on CNN |
| Deep Learning Methods | Activation Function | Tensorflow, pytorch, Keras |
| Deep Learning Application | Forward and Backward propagation | Batch Normalization, dropout |
| Artificial Neural Network | Deep Learning Libraries | Performance measure for ANN |
| CNN architecture | CNN for computer vision | Object detection |
| Computer vision basics | OpenCV | Working with Images |
| Natural Language Processing | ||
|---|---|---|
| Natural Language processing | NLP Applications | Regular Expression |
| Tokenization, stopwords | Stemming, Lemmatization | Word Embeddings |
| NLTK | POS Tagging NER | bag of words, TF-IDF, unigrams, bigrams |
| RNN, RNN architecture | Bidirectional LSTM – Encoders and Decoders | Text classification using ML |
| Generative AI & Agentic AI | ||
| Introduction to Gen AI | Rule-based vs neural generation | Generative Adversarial Network |
| Variable Auto Encoder | Transformers | Application of Generative AI, Ethics |
| 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 |
| Retrieval-Augmented Generation (RAG) | Agentic Architectures & Autonomous Workflows | Multi-step Reasoning |
| Project | ||
| Apply statistical methods to make decision in various business problems, including bank, stock market | Apply regression to predict future flight price | Apply classification to classify customer churn |
| Streamlit Framework | Deployment using Hugging Face | Model and agent deployment |
