Full Stack AI Engineer Course

Core Concepts:
Introduction to AI and Full Stack AI Engineering.
Python Programming for AI.
Probability, Statistics, and Linear Algebra basics

Tools:
Python, Jupyter Notebooks, Git
Project: Data wrangling and exploratory data analysis on a public dataset (e.g., Kaggle or UCI).

Core Concepts:
Data collection, cleaning, and transformation.
Introduction to SQL and NoSQL databases.
Data pipelines and ETL processes.
Data warehousing with cloud platforms (Azure, AWS)

Tools:
SQL, Pandas, PySpark, Airflow, Databricks

Project:
Create an end-to-end ETL pipeline, ingesting and transforming a dataset from multiple sources.

Core Concepts:
Supervised and Unsupervised Machine Learning.
Deep Learning with neural networks (CNNs, RNNs).
Model selection, hyperparameter tuning, and cross-validation.

Tools:
Scikit-Learn, TensorFlow, PyTorch

Project:
Train and tune machine learning and deep learning models on image or text data, documenting model performance metrics.

Core Concepts:
Introduction to MLOps and CI/CD for ML.
Containerization with Docker and Kubernetes.
Deploying models on cloud platforms (AWS SageMaker, Azure ML, Google AI Platform).

Tools:
Docker, Kubernetes, Flask/FastAPI, AWS SageMaker, Azure ML, Google Cloud

Project:
Deploy a machine learning model as a REST API in a cloud environment and build a CI/CD pipeline for model updates.

Core Concepts:
Building front-end interfaces for AI applications
Integrating ML models with back-end and front-end systems
User experience considerations for AI applications

Tools:
React.js, Flask/FastAPI, REST APIs

Project:
Build a web application with a user interface that interacts with a deployed AI model, e.g., an image classifier or sentiment analyzer.

Core Concepts:
Model monitoring and maintenance in production
Managing data drift and model retraining
Logging, alerting, and anomaly detection

Tools:
Prometheus, Grafana, MLflow, Apache Kafka

Project:
Set up a monitoring and alerting system for a deployed model, including logging and metrics tracking.

Objective:
Build a complete AI application from scratch, covering data collection, model training, deployment, and monitoring.

Options:
Choose a business-relevant problem
(e.g., sentiment analysis, recommendation engine, image classification).

Outcome:
Participants submit a fully documented project with code, a working deployment, and monitoring setup.