Live Projects in techcadd's Machine Learning Course

The Machine Learning Course at techcadd focuses on practical learning through real datasets, hands-on assignments, trained models, APIs and portfolio-ready projects. Students progress from Python and data analysis to Machine Learning, Deep Learning, Generative AI and deployment.

What Kind of Projects Do Students Build?

Depending on the selected 3, 6 or 9-month track, students work on projects covering:

• Python programming

• Data analysis and visualisation

• Machine Learning

• Deep Learning

• Computer Vision

• Generative AI

• API development

• Deployment

1. Python & Data Analysis Projects

Students begin with Python and work with datasets using NumPy and Pandas.

Projects can include:

• Data cleaning

• Missing-value handling

• Data analysis

• EDA reports

• Visualisations

• Statistical analysis

2. Machine Learning Projects

Students build and evaluate models for real-world problems such as:

• House-price prediction

• Loan-approval prediction

• Regression

• Classification

• Model comparison

• Customer segmentation

They also learn preprocessing, feature engineering and model evaluation.

3. Deep Learning & Computer Vision Projects

Advanced tracks include projects using TensorFlow, Keras, OpenCV and YOLO.

Students can work on:

• ANN and CNN models

• Image classification

• LSTM forecasting

• YOLO object detection

• Computer Vision applications

4. Generative AI Projects

The 9-month Expert track introduces modern AI application development through:

• LLM applications

• Prompt engineering

• Hugging Face

• RAG pipelines

• Vector databases

• AI agents

• Tool calling

Students can build applications such as document-based question-answering systems and LLM-powered solutions.

5. Machine Learning API & Deployment

Students learn how to take models beyond notebooks by developing APIs using:

• Flask

• FastAPI

• REST APIs

• Database integration

• Frontend integration

• Cloud deployment

6. End-to-End AI/ML + GenAI Capstone

The advanced track brings multiple technologies together in a final capstone project involving Machine Learning, Deep Learning, Generative AI, APIs, databases and deployment.

Build a Practical Portfolio

Students can finish the programme with portfolio-ready work such as:

• Python notebooks

• EDA reports

• Machine Learning models

• Deep Learning projects

• Computer Vision projects

• LLM applications

• RAG pipelines

• AI agents

• Deployed applications

• Final capstone project

These projects can be presented during interviews, internships, academic presentations and freelance discussions.

Projects Across Three Tracks

3-Month Practitioner Track

Python, data handling, visualisation, classical ML, Deep Learning basics and API deployment.

6-Month Professional Track

Adds statistics, feature engineering, unsupervised learning, NLP, Computer Vision and SQL-backed deployment.

9-Month Expert Track

Adds RNN, LSTM, GRU, Generative AI, LLMs, RAG, vector databases, AI agents, production deployment and an end-to-end capstone.

From Learning to Deployment

Learn → Build → Evaluate → Deploy → Present

This approach helps students turn concepts into practical projects and build evidence of their technical skills.

Who Can Work on These Projects?

The programme is suitable for:

• Students after 12th

• Graduates and final-year students

• Working professionals

• Career changers

• Beginners without coding experience

• Aspiring Data Scientists

• Aspiring ML Engineers

• Aspiring AI Engineers

Start Building Machine Learning Projects With techcadd

Move beyond theoretical learning and build practical projects in Machine Learning, Deep Learning, Generative AI and deployment.

Book a Free Demo and explore the Machine Learning programme at techcadd.