Weekend IT & AI Career Courses
Comprehensive in-person weekend courses (60 Classes / 120 Hours each) designed on a Saturday Learn (2h) + Sunday Build (2h) model with hands-on live coding practice at our Bagnan campus.
Course 1 — AI/ML Foundation (60-Class Career Track)
60 Weekend Classes (120 Hours). Python Programming, Mathematics for AI/ML, Statistics, Data Handling & EDA, Introduction to AI, and Foundation Projects.
- ▸ Module 1: Write clean Python programs, OOP, NumPy, Pandas and CSV data manipulation
- ▸ Module 2: Understand algebra, vectors, matrices, calculus, and mathematical intuition for ML
- ▸ Module 3: Perform statistical calculations, variance, probability, distributions and correlation
Course 2 — Advanced AI & Machine Learning (60-Class Career Track)
60 Weekend Classes (120 Hours). ML Fundamentals, Regression, Classification, Unsupervised Learning, Model Optimization, Deep Learning, Modern AI & Capstone.
- ▸ Module 1: Machine Learning lifecycle, supervised, unsupervised, reinforcement, train/val/test splits, bias-variance
- ▸ Module 2: Regression models, Linear, Multiple, Polynomial, MAE, MSE, RMSE, R² and House Price Prediction project
- ▸ Module 3: 7 Classification algorithms, Decision Trees, Random Forest, SVM, Confusion Matrix, ROC-AUC and Churn Prediction
Course 1 — Cloud & DevOps Engineer (60-Class Career Track)
60 Weekend Classes (120 Hours). Linux Administration, Computer Networking, AWS Cloud Engineering, Docker, CI/CD, and Kubernetes on AWS EKS.
- ▸ Module 1: Master Linux administration, Bash automation, Networking, Git & Web Server Deployment
- ▸ Module 2: Build AWS Cloud architectures, IAM, EC2, VPC, ALB, Auto Scaling, RDS, and CloudWatch
- ▸ Module 3: Containerize with Docker, Docker Compose, GitHub Actions CI/CD, and Jenkins pipelines
Course 2 — MLOps & Production AI Engineering (60-Class Career Track)
60 Weekend Classes (120 Hours). FastAPI Model Serving, MLflow Tracking, DVC, Feast Feature Store, CI/CD/CT Pipelines, Kubeflow, and Drift Monitoring.
- ▸ Module 1: High-performance ML serving with FastAPI, Pydantic validation, async endpoints, and Docker containerization
- ▸ Module 2: Experiment tracking, parameter logging, and enterprise model registry with MLflow on AWS
- ▸ Module 3: Data version control (DVC), Great Expectations validation, and Feast feature store architecture