AI & ML Stack Development

Build Intelligent, End-to-End AI Applications

Learn Python, Machine Learning, Deep Learning, NLP, MLOps and Full-Stack development to build production-ready AI-powered web applications.

View Course Roadmap Explore Tech Stack

36 Weeks Deep Learning Journey

8+ Major AI Projects

Full Stack AI to Deployment

Why AI/ML Full-Stack?

AI is transforming healthcare, finance, retail, security and more. This course prepares you to build AI systems end-to-end: from data and models to web dashboards and APIs.

Hands-on ML & Deep Learning
React-based AI dashboards
Flask / FastAPI model serving
MLOps, Docker & Cloud deployment

Course Roadmap

A structured, 10-phase journey from Python foundations to advanced AI apps deployed in production.

Phase 1 · Python & Mathematics
Weeks 1–4

Python fundamentals, OOP, file handling, modules
NumPy, Pandas, data cleaning & preprocessing
Maths for ML: linear algebra, calculus, statistics
Probability, hypothesis testing, correlation & regression

Phase 2 · Data Analysis & Visualization
Weeks 5–7

EDA, missing data handling, outlier detection
Feature engineering and data transformation
Matplotlib, Seaborn, Plotly dashboards
Data storytelling & visualization best practices

Phase 3 · Machine Learning Fundamentals
Weeks 8–13

Supervised ML: Regression, Trees, RF, SVM, KNN, Naive Bayes, Gradient Boosting
Unsupervised ML: K-Means, Hierarchical, DBSCAN, PCA, association rules
ML pipeline with Scikit-learn
Train-test split, scaling, model selection & persistence

Phase 4 · Deep Learning
Weeks 14–17

ANNs: activations, backpropagation, gradient descent, loss functions
TensorFlow, Keras, PyTorch model building
CNNs for images, transfer learning & object detection
RNNs, LSTMs, GRUs for sequential/NLP tasks

Phase 5 · Natural Language Processing
Weeks 18–19

Text preprocessing, tokenization, stemming, lemmatization
BoW, TF-IDF, embeddings
NER, text classification, sentiment analysis
Transformers, BERT & modern language models

Phase 6 · Frontend for AI Apps
Weeks 20–23

HTML5, CSS3, JavaScript ES6+, responsive design
React.js components, hooks, state management
API integration, file uploads, real-time UI
Data visualisation with Chart.js / D3.js

Phase 7 · Backend & Model Serving
Weeks 24–27

Flask: routing, Jinja2, REST APIs, ML endpoints
FastAPI: async APIs, Pydantic models, docs, auth
Model deployment via APIs & validation
Batch vs real-time predictions, versioning & A/B tests

Phase 8 · Databases & MLOps
Weeks 28–29

PostgreSQL, MongoDB, Redis, vector databases
Storing predictions, logs & telemetry
DVC, MLflow, model tracking & versioning
Docker, model serving & retraining pipelines

Phase 9 · Cloud & Deployment
Week 30

AWS SageMaker, Azure ML, Google Cloud AI overview
Serverless ML APIs
Kubernetes basics & CI/CD for ML apps
Monitoring, scaling & cost optimisation

Phase 10 · Advanced Projects
Weeks 31–36

Sentiment Analysis Web App (Python, NLP, Flask, React, MongoDB, LSTM/BERT)
Image Classification App (CNN, TensorFlow, React, Transfer Learning)
Recommendation System (Collaborative/Content-based)
Chatbot Application (Transformers, FastAPI, React, Socket.io)
Predictive Analytics Dashboard (Time Series)
Face Recognition System (CNN, OpenCV)
AI-Powered Content Generator (GPT + FastAPI)
Capstone AI Application (complex full-stack AI)

Technology Stack You’ll Master

A powerful combination of AI, web and MLOps tools used by modern product and research teams.

Machine Learning & AI

Tools & Libraries:

Python 3.x, Scikit-learn, TensorFlow, Keras, PyTorch, XGBoost, LightGBM, NLTK, spaCy, Hugging Face Transformers, OpenCV.

Data Processing & Analysis

Libraries & Workbench:

NumPy, Pandas, Matplotlib, Seaborn, Plotly, Jupyter Notebooks and data preprocessing utilities.

Frontend Stack

UI & Visualisation:

React.js, Chart.js, D3.js, Material UI, Axios and modern responsive web patterns.

Backend & APIs

Application Layer:

Flask, FastAPI, Django (optional), REST APIs, WebSockets, real-time communication.

Databases

Storage & Retrieval:

PostgreSQL, MongoDB, Redis and vector databases like Pinecone / Weaviate.

MLOps & Deployment

Production Tools:

Docker, MLflow, DVC, AWS SageMaker, Azure ML, Google Cloud AI and Kubernetes basics.

Career Opportunities & Industries

Move into high-impact AI roles with a strong portfolio of deployed projects.

Roles You Can Target

ML Engineer
AI Full-Stack Developer
Data Scientist (with deployment skills)
ML Operations Engineer (MLOps)
AI Application Developer
Computer Vision Engineer
NLP Engineer
AI Product Developer

Industries Hiring AI Talent

AI/ML Product Companies & Startups
FinTech & Banking Analytics
Healthcare & Medical Imaging
E-commerce & Recommendation Systems
Automotive & Autonomous Systems
Security & Surveillance
EdTech & Smart Learning
Digital Marketing & AdTech

Course Details & Outcomes

Everything you need to know before joining the AI & ML Full-Stack Development program.

Course Duration & Format

Duration: 36 weeks (9 months)
Batch Options: Weekday (Mon–Fri, 2–3 hrs/day) or Weekend (Sat–Sun, 5–6 hrs/day) plus fast-track options
Mode: Classroom with GPU access, Online Live, Hybrid, Recorded lectures

Prerequisites

Basic programming knowledge
High-school level mathematics
Logical thinking & problem-solving attitude
Passion for AI and technology
No prior ML experience required

Certification

Airocode AI/ML Full-Stack Developer Certificate
Machine Learning Specialist Certificate
Deep Learning Proficiency Certificate
8 project completion certificates
Optional research paper guidance
Portfolio of real AI applications

Placement Support

AI-focused placement: resume building & ML interview prep
Math/stat revision & system design practice
25+ mock interviews & portfolio showcasing
Kaggle competitions & startup connections
100+ AI-focused hiring partners
Placement stats: 80%+ placement, avg 7–12 LPA, up to 25 LPA