Applied Data Science with AI/ML – 150 Hrs
Level 1 – 60 Hrs
Level 2 – 60 Hrs
Level 3 – 30 Hrs
Modules
Level 1 – Data Analysis 60 Hrs:
- Exploratory Data Analysis using Pandas
- Machine Learning
- Python (Pre-recorded video)
- Deep Learning
- Natural Language Processing (NLP)
- Foundational AI/ML Tools
- GenAI tools and AI Agents
Program Objectives
To equip learners with industry-relevant technicalskills and
enhance theirjob readiness through project-based learning,
hands-on tool exposure, and real-world application
deployment, thereby preparing them for successful
employment in core domain areas
Program Outcomes
- Perform exploratory data analysis(EDA) using Pandas including data cleaning with proficiency in handling large datasets.
- Build and evaluatemachine learningmodels, including regression, classification, and clustering algorithms, and apply them to solve practical business problems.
- Develop projectsthat demonstrate the ability to apply learned concepts
Project Stream:
• Machine Learning and Deep Learning Deploy
using Streamlit or Huggingface
Experiential Project Based Learning
Tools / Platform:
• Google Colab /Jupyter Notebook
• OpenAI, Gemini, Copilot
• HuggingFace, Langchain, CrewAI
| Module 1: Exploratory Data Analysis with Pandas (30 Hours) | ||
|---|---|---|
| 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 | SelectionData (loc, iloc) | Filtering Data Frames |
| Sorting Data Frames | Importing Data | Data Visualization |
| Working with Categorical Data | Grouping & Aggregation | Merging Data Frame (concat, merge) |
| Module 2: Machine Learning (30 Hours) | ||
| Understand what is 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 | MSE, R-Squared, MAE, SSE | Performance Measure for Classification |
| Classification | Various types of classification | Accuracy, Recall, Precision, F1 measure |
| Sorting Data Frames | Importing CSV files | Importing Excel Files |
| LEVEL 2: Deep Learning and NLP (60 Hrs) | ||
| Module 3: Deep Learning (30 Hrs) | ||
| 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 |
| Module 4: Natural Language Processing (30 Hrs) | ||
| 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 | ||
| Project: Machine Learning and Deep Learning | ||
| Apply statistical methods to make decisions in various business problems, including bank, stock market | Apply regression to predict future flight price | Apply classification to classify customer churn |
| LEVEL 3: AI ML Tools (30 Hrs) | ||
| Module 5: Foundational AI & ML Tools (10 Hrs) | ||
| Data science workflow | Automated data profiling | Hands-on report generation |
| Automated Machine Learning with PyCaret | Automated data preprocessing | Automated ML Models |
| Module 6: Generative AI and LLM Tools (10 Hrs) | ||
| Intro to GenAI, LLMs | Prompt Engineering techniques | Hands-on API usage |
| Explore Hugging Face, Copilot | Retrieval-Augmented Generation (RAG) | Vector Databases, embeddings |
| Module 7: AI Agents and Advanced Topics (10 Hrs) | ||
| Core agent concepts | Leading agent frameworks | Build a basic agent |
| Multi-Agent Systems | Collaborative CrewAI project | Model and agent deployment |
