Diploma in Artificial Intelligence & Digital Skills

Understand AI, machine learning, deep learning, generative AI, and automation in plain language

Explore how AI is used in education, business, marketing, finance, healthcare, and software

Identify realistic entry-level AI career paths and skill expectations

Recognize where AI adds value and where traditional tools are better

Create a personal AI learning and portfolio roadmap

Understand datasets, variables, labels, features, bias, and data quality

Collect, clean, and structure small datasets for analysis

Use spreadsheets and notebooks to inspect data patterns

Read charts, summaries, correlations, and outliers correctly

Build a simple data story from a messy dataset

Write Python scripts using variables, functions, loops, lists, dictionaries, and files

Use notebooks for experimentation and documentation

Work with Pandas, NumPy, and basic visualization libraries

Debug Python errors and interpret stack traces

Create reusable code for importing, cleaning, and summarizing data

Understand training, testing, validation, overfitting, and evaluation

Compare classification, regression, clustering, and recommendation use cases

Prepare features and labels for simple models

Measure performance using practical metrics

Document model decisions and limitations clearly

Build models for prediction and classification tasks

Use train/test splits and basic model comparison

Interpret accuracy, precision, recall, and confusion matrices

Improve models through feature selection and data cleaning

Complete a small supervised learning portfolio project

Use clustering to segment customers, content, or behavior

Explore dimensionality reduction concepts without heavy theory

Find patterns in unlabeled datasets

Translate model outputs into business-friendly recommendations

Prepare a short insight report from unsupervised analysis

Use AI assistants for research, summarizing, ideation, and planning responsibly

Write prompts with context, constraints, examples, and evaluation criteria

Create repeatable workflows for study, business, and content tasks

Check AI outputs for accuracy, bias, and missing assumptions

Build a personal prompt library and AI productivity workflow

Clean and structure text data for analysis

Use sentiment, classification, summarization, and extraction workflows

Evaluate language model responses against clear criteria

Design practical text automation for enquiries, reviews, or documents

Build a mini NLP workflow with documented limitations

Understand image classification, object detection, OCR, and visual search use cases

Use pre-trained tools and models for practical image workflows

Prepare image inputs and evaluate output quality

Consider privacy, consent, and safety when working with visual data

Create a small computer vision application demo

Identify bias, privacy, transparency, copyright, and accountability risks

Select an AI problem worth solving and define success measures

Build or prototype an AI-enabled solution using real-world constraints

Prepare documentation covering data, model, risks, and user guidance

Present the capstone with recommendations for responsible deployment