Course Overview

The gap between knowing machine learning theory and being able to build a production ML system is wider than most courses acknowledge. CETPA Infotech's Project-Based Deep Learning and Machine Learning with Python Training in Noida was designed specifically to close that gap. From day one, the course is structured around building real AI systems — not toy examples, but functioning applications that could be launched to real users. You will work in project teams, manage a git repository, iterate on your models based on evaluation metrics, and present your systems to a technical panel at the end of each project sprint.

The programme runs four major project tracks in parallel with theory modules, so that every concept you learn is immediately applied in a living codebase. Project Track 1 is a computer vision system for product quality inspection. Track 2 is an NLP application for customer support automation. Track 3 is a demand forecasting system for a retail supply chain. Track 4 is a real-time anomaly detection system for financial transactions. Each track covers different ML and DL techniques, ensuring broad exposure across the most commercially valuable AI domains.

Collaboration is a core skill the course develops. Teams of three to four trainees work together using agile sprints, conduct peer code reviews, and resolve integration issues — replicating the real-world experience of working on an AI engineering team. CETPA's industry mentors attend bi-weekly code reviews and challenge teams to defend their architectural decisions, build the critical thinking that turns good coders into great engineers. Graduates consistently secure AI Engineer, ML Engineer, and Applied Scientist roles with packages 30–40% above the market average for new graduates.

Course Curriculum

• Agile AI Engineering: Sprints, User Stories, Code Reviews
• Git & MLflow for AI Project Management
• Project Track 1: Computer Vision QA System (CNN, OpenCV)
• Project Track 2: NLP Customer Support Bot (BERT, Hugging Face)
• Project Track 3: Retail Demand Forecasting (LSTM, Prophet)
• Project Track 4: Financial Anomaly Detection (Isolation Forest, Autoencoder)
• Model Evaluation Deep Dive: Precision/Recall Trade-offs in Production
• Containerisation with Docker for AI Apps
• CI/CD for ML Pipelines with GitHub Actions
• Cloud Deployment: AWS SageMaker / GCP Vertex AI
• Final Demo Day: Present AI Systems to Industry Panel