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Duration
9 Months
Modules
18 Modules
Projects
Real-Time
Support
Interview Prep
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Module 01
Introduction to AI / ML
- What is Artificial Intelligence?
- What is Machine Learning?
- AI vs ML vs Deep Learning
- Applications of AI in real world
- Overview of career paths in AI / ML
Module 02
Python for AI / ML
- Python basics and syntax
- Functions, loops and conditional statements
- List, tuple, dictionary and set
- File handling and exception handling
- Object-oriented programming basics
Module 03
Mathematics for Machine Learning
- Basic statistics and probability
- Mean, median, mode and standard deviation
- Linear algebra basics
- Matrices and vectors
- Introduction to calculus for optimization
Module 04
Data Analysis with NumPy and Pandas
- Introduction to NumPy arrays
- Pandas Series and DataFrame
- Data cleaning and preprocessing
- Handling missing values
- Filtering, grouping and transformation
Module 05
Data Visualization
- Introduction to Matplotlib
- Seaborn basics
- Bar chart, line chart, histogram and scatter plot
- Visualizing correlations and distributions
- Storytelling with data
Module 06
Machine Learning Fundamentals
- Supervised and unsupervised learning
- Training data and testing data
- Features and target variables
- Model training workflow
- Introduction to scikit-learn
Module 07
Supervised Learning Algorithms
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- K-Nearest Neighbors
Module 08
Unsupervised Learning Algorithms
- Clustering concepts
- K-Means clustering
- Hierarchical clustering
- Dimensionality reduction basics
- PCA introduction
Module 09
Model Evaluation and Optimization
- Accuracy, precision, recall and F1-score
- Confusion matrix
- Train-test split and cross validation
- Overfitting and underfitting
- Hyperparameter tuning basics
Module 10
Deep Learning Basics
- Introduction to neural networks
- Perceptron and multilayer perceptron
- Activation functions
- Introduction to TensorFlow / Keras
- Building simple deep learning models
Module 11
NLP, Computer Vision and Real-Time Projects
- Introduction to Natural Language Processing
- Text preprocessing and sentiment analysis
- Introduction to Computer Vision
- Image classification basics
- Real-time AI / ML mini projects
Module 12
Flask and FastAPI for AI Applications
- Flask fundamentals for simple ML web apps
- FastAPI setup, routing and request handling
- Creating REST APIs for trained ML models
- Input validation with Pydantic schemas
- API testing using Postman and Swagger UI
Module 13
Advanced Deep Learning
- Convolutional Neural Networks basics
- Recurrent Neural Networks overview
- Transfer learning concepts
- Model regularization techniques
- Working with real-world deep learning datasets
Module 14
Generative AI and LLM Basics
- Introduction to generative AI
- Large language model fundamentals
- Prompt engineering basics
- Using AI APIs in applications
- Building simple GenAI use cases
Module 15
MLOps and Model Lifecycle
- Understanding MLOps workflow
- Experiment tracking basics
- Model versioning concepts
- Monitoring model performance
- Retraining and maintenance basics
Module 16
Docker, Cloud and FastAPI Deployment
- Dockerizing Flask and FastAPI applications
- Serving ML models through FastAPI endpoints
- Environment variables and dependency management
- Deployment with Gunicorn, Uvicorn and Nginx
- Cloud deployment workflow and production best practices
Module 17
Capstone AI / ML Projects
- End-to-end machine learning project
- NLP or computer vision project
- Data preprocessing to deployment workflow
- Project documentation and GitHub upload
- Portfolio-ready project presentation
Module 18
Career Preparation and Mock Interviews
- AI / ML resume building
- LinkedIn and GitHub profile improvement
- Scenario-based interview preparation
- Technical mock interviews
- Placement support and career guidance
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