Learn modern computer science, A to D

In plain words: AI is software that learns patterns from examples instead of following rules you type out by hand. You start with how machines represent knowledge, then train simple models, then work up to the neural networks behind today's chatbots and image tools.

What you'll be able to do

• How a model is trained, tested and how it goes wrong (overfitting, bias, hallucination)
• Machine learning and deep learning basics with the maths explained in words first
• Large language models, prompting, embeddings, RAG and AI agents
• Responsible AI: privacy, evaluation and where a human must stay in the loop

Useful for: ML engineer, AI application developer, data-savvy product roles.

In plain words: A blockchain is a shared record that many computers keep in sync, so nobody has to trust one company to hold the truth. You learn how blocks, keys and transactions work, then write and deploy real smart contracts on a free test network.

What you'll be able to do

• Hashing, keys, wallets, transactions and how consensus keeps everyone honest
• Ethereum and the EVM, plus Solidity from your first contract onward
• Gas and fees, Layer 2s, token standards, NFTs and DeFi building blocks
• Smart-contract security: the classic exploits and how audits catch them

Useful for: smart-contract developer, Web3 full-stack, protocol analyst.

In plain words: Cloud computing means renting computers, storage and networking on demand instead of buying servers. You learn what actually runs your app, how to package it, and how to ship changes safely and repeatably.

What you'll be able to do

• Compute, storage, networking and identity — the four things every cloud sells
• Linux, containers, Docker and Kubernetes explained from zero
• Infrastructure as code, CI/CD pipelines and environment separation
• Reliability and cost: monitoring, scaling, backups and security basics

Useful for: DevOps, platform and site-reliability engineering.

In plain words: Data work is turning raw records into answers people can act on. You learn to store data properly, query it, clean it, and present it — the groundwork every analytics and AI project stands on.

What you'll be able to do

• SQL and relational modelling, plus when NoSQL fits better
• Pipelines, warehouses and the difference between batch and streaming
• Cleaning, statistics and honest visualisation of results
• Data quality, governance and privacy so your numbers can be trusted

Useful for: data analyst, analytics engineer, data engineer.