Course Duration
01
4 Months
Structured learning path
AI / ML + Generative AI
Python • Machine Learning • Deep Learning • NLP • Computer Vision • MLOps
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Curriculum
02
18 Modules
Step-by-step learning
Hands-On
03
Real-Time Projects
Build practical applications
Career Support
04
Interview Prep
Placement-focused guidance
Complete Curriculum
AI / ML Learning Roadmap
18 practical modules planned across a 16-week learning path. Click any module to view the topics covered.
Modules
Month 1
Month 2
Month 3
Month 4
Learning Plan
W1
W2
W3
W4
W5
W6
W7
W8
W9
W10
W11
W12
W13
W14
W15
W16
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
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
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
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
Data Visualization
- Introduction to Matplotlib
- Seaborn basics
- Bar chart, line chart, histogram and scatter plot
- Visualizing correlations and distributions
- Storytelling with data
Machine Learning Fundamentals
- Supervised and unsupervised learning
- Training data and testing data
- Features and target variables
- Model training workflow
- Introduction to scikit-learn
Supervised Learning Algorithms
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- K-Nearest Neighbors
Unsupervised Learning Algorithms
- Clustering concepts
- K-Means clustering
- Hierarchical clustering
- Dimensionality reduction basics
- PCA introduction
Model Evaluation and Optimization
- Accuracy, precision, recall and F1-score
- Confusion matrix
- Train-test split and cross validation
- Overfitting and underfitting
- Hyperparameter tuning basics
Deep Learning Basics
- Introduction to neural networks
- Perceptron and multilayer perceptron
- Activation functions
- Introduction to TensorFlow / Keras
- Building simple deep learning models
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
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
Advanced Deep Learning
- Convolutional Neural Networks basics
- Recurrent Neural Networks overview
- Transfer learning concepts
- Model regularization techniques
- Working with real-world deep learning datasets
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
MLOps and Model Lifecycle
- Understanding MLOps workflow
- Experiment tracking basics
- Model versioning concepts
- Monitoring model performance
- Retraining and maintenance basics
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
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
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
Select a module to view the detailed topics.
Hands-on Learning
Capstone Projects
Practice real application flows while building projects you can discuss during interviews.
We Learn – Groceries Application
We Learn – Food Application
We Learn – Banking Application
We Learn – Delivery Application
We Learn – Airlines Application
We Learn – LMS Application
Career Preparation
Build Job-Ready Profiles
Present your skills professionally across the platforms recruiters commonly review.
GitHub
Showcase code, projects, repositories and consistent development work.
Build a stronger professional identity and improve recruiter visibility.
Portfolio
Present your projects, technical skills and practical experience clearly.
AI Skills
Learn the AI Tools Used by Modern Developers
Use AI to code faster, solve problems, build projects, prepare for interviews and stay ready for modern software jobs.
✦ChatGPT
AIClaude
GeminiGitHub Copilot
Cursor AI
PerplexityAIOpenAI API
LangChain
🤗Hugging Face
Replit AIWindsurf AI
AI Agents
AI for Coding
Write, debug and understand code faster with modern AI coding assistants.
AI for IT Jobs
Prepare resumes, interviews, projects and workplace tasks using AI.
AI for Career Growth
Stay updated with tools used by developers, startups and product teams.
Student Outcomes
Our Alumni
Surya
Infosys
Farhath
L&T
Manasa
Capgemini
Sana Lekana
Tech Mahindra
Ganesh
Genpact
Gnaneshwar
Amazon
Ravi
Hexaware
Harinath
AdobeSurya
InfosysFarhath
L&TManasa
CapgeminiSana Lekana
Tech MahindraGanesh
GenpactGnaneshwar
AmazonRavi
HexawareHarinath
AdobeRavi
HexawareHarinath
AdobeSurya
InfosysFarhath
L&TManasa
CapgeminiSana Lekana
Tech MahindraGanesh
GenpactGnaneshwar
AmazonRavi
HexawareHarinath
AdobeSurya
InfosysFarhath
L&TManasa
CapgeminiSana Lekana
Tech MahindraGanesh
GenpactGnaneshwar
Amazon
Common Questions
Frequently Asked Questions
Everything you need to know about our AI / ML Training Program.
You will learn Python, mathematics for machine learning, NumPy, Pandas, data visualization, supervised and unsupervised learning, model evaluation, deep learning, NLP, computer vision, Flask, FastAPI, Generative AI, MLOps, deployment and capstone projects.
No. The curriculum starts with AI/ML fundamentals, Python basics and mathematical foundations before moving into machine learning and deep learning.
Yes. The course includes Python syntax, functions, data structures, file handling, exceptions and object-oriented programming, followed by NumPy and Pandas for analysis.
Yes. It includes linear regression, logistic regression, decision trees, random forest, K-nearest neighbors, K-means clustering, hierarchical clustering and PCA basics.
Yes. The curriculum covers neural networks, TensorFlow/Keras, CNNs, RNNs, transfer learning, NLP preprocessing and sentiment analysis, plus computer vision and image classification basics.
Yes. One module covers Generative AI, large language model fundamentals, prompt engineering, AI APIs and simple GenAI use cases.
Yes. The curriculum includes Flask, FastAPI, Docker, Gunicorn, Uvicorn, Nginx, cloud deployment, experiment tracking, model versioning, monitoring, retraining and model lifecycle basics.
The final modules include end-to-end AI/ML projects, NLP or computer vision projects, deployment workflow, GitHub documentation, portfolio presentation, resume building, LinkedIn/GitHub improvement and technical mock interviews.
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