Overview
What this course covers.
The Diploma in AI takes you from programming fundamentals to building real, working AI and machine learning projects - no prior AI background required.
You will learn Python for data work, core machine learning concepts, and how to build, train, and deploy practical AI models, finishing with a portfolio of real projects you can show employers or clients.
This diploma suits students and professionals who want a genuine, hands-on entry point into one of the fastest-growing fields in technology.
artificial intelligence
machine learning
diploma
python
Outcomes
Skills you will build.
- Program confidently in Python for data and AI work
- Understand core machine learning concepts and workflows
- Build and train practical machine learning models
- Complete a portfolio of real AI projects
Curriculum Preview
Modules and lessons.
This is a public preview only. Full LMS delivery and progress tracking will be prepared in a later step.
01 Python Programming Foundations 3 lessons
- Python Fundamentals for Data Work Variables, types, and control flow, built for AI from day one.
- Functions & Core Data Structures Lists, dictionaries, and writing code you'll actually reuse.
- Files, Errors & Clean Code Habits Read real data and fail gracefully instead of crashing.
02 Data Handling & Analysis (NumPy & Pandas) 3 lessons
- NumPy for Fast Numerical Computing The array operations every AI pipeline is built on.
- Cleaning & Exploring Data with Pandas Turn messy real-world data into something usable.
- Visualizing Data to Find Patterns Charts that reveal what raw numbers hide.
03 Applied Mathematics for Machine Learning 2 lessons
- Statistics Essentials for ML Mean, variance, and distributions, taught through real datasets.
- Linear Algebra, the Practical Way Vectors and matrices, understood through what ML actually uses them for.
04 Machine Learning Fundamentals 4 lessons
- What Machine Learning Actually Is Supervised vs unsupervised learning, with real, working examples.
- Building Your First Models Regression and classification with scikit-learn, from data to prediction.
- Evaluating Models Honestly Accuracy, precision/recall, and catching overfitting before it costs you.
- Feature Engineering That Improves Results Prepare data in the ways that actually move model performance.
05 Deep Learning & Neural Networks 3 lessons
- How a Neural Network Learns The intuition behind deep learning, without the intimidating math wall.
- Building & Training Models with TensorFlow Hands-on model construction, from architecture to training.
- Working with Images & Text A practical first look at CNNs and NLP.
06 Model Deployment & Productionization 2 lessons
- Saving & Serving a Trained Model Turn a notebook experiment into something usable outside it.
- Wrapping a Model in a Simple App Give your model an interface a real user can actually interact with.
07 Capstone AI Project 2 lessons
- Choosing a Project Worth Building Pick a dataset and problem that's genuinely portfolio-worthy.
- Building, Training & Presenting Your Project Finish a real AI project you can demo with confidence.
Requirements
Before you start.
- Basic computer literacy
- No prior programming experience required
Tools
Tools covered.
Python
Jupyter Notebook
TensorFlow / scikit-learn
Pandas & NumPy
FAQ
Common questions.
FAQ will be added soon.
