Choose a focused Python, web development, data science or TensorFlow deep learning path for your cohort. Each course pairs short explanations with guided labs, independent practice and a project learners can demonstrate.
These are separate options, not a promise to cover every topic in one short workshop. We agree the level and depth after checking learners' starting skills and available hours.
Build confidence with variables, control flow, functions, collections, files, exceptions and testing through small problems.
Learn HTTP, routes, forms, data storage and safe input handling by building one working application. Flask suits a smaller app or API; Django suits a more structured, database-backed course.
Use Python, pandas and visualisation to explore data, then train and evaluate an introductory model without hiding its limitations.
Build a small neural network with TensorFlow and Keras, interpret training and validation results, and discuss overfitting and model limitations.
A college can select one path or sequence them across modules. TensorFlow deep learning is a separate follow-on course; production deployment and research-level mastery need additional time and scope.
This sample 30-hour format builds a common base before learners choose a later web or ML specialisation. It is adaptable, not a fixed syllabus or confirmed booking.
Run programs, trace variables and practise conditionals and loops.
Break problems into functions; use lists, dictionaries and tests.
Read CSV files, validate values and summarise results.
Plan inputs, implement a program, handle errors and review code.
Improve the project, explain decisions and preview web or data applications.
Breaks are arranged with the institution. The 30 teaching hours above do not include full Flask, Django, ML or TensorFlow deep learning training.
Learners run each example, change an input and explain what changed before moving on.
Short questions and code reviews reveal gaps early; mistakes become a prompt to retry with feedback.
Each learner or team shows the working output, decisions made and one limitation or improvement.
Live virtual delivery is available across time zones. On-site delivery, including international visits, depends on agreed travel and applicable permissions. Learners need a laptop and a working Python environment; web, ML and TensorFlow paths may need additional packages, internet access and suitable compute resources. A short readiness check helps set the correct level. Cohort size, schedule, assessment, materials, travel and fee are agreed in a written proposal.
I have delivered Python, Django, Flask, data analytics and AI-related training for engineering and professional cohorts. The Career Profile provides dated examples. Course descriptions here are options for discussion, not endorsements or measured learner outcomes.
Tell me the student year, current Python level, preferred path, cohort size, available hours, dates and lab facilities. I’ll propose a focused syllabus and fee for your institution.
The form opens in Google Forms. A click is not counted as a submitted enquiry.