Learning path

Code from zero

Never coded? Learn Python in your browser, then build your first machine-learning models.

24 lessons · 137 min left

Start path → What AI actually is

FoundationsLesson 1: What AI actually isBefore we can keep AI safe, we need to know what it is. Short answer: it is a machine that guesses really, really well.Beginner4 min read🧪 Lab📝 TestUp next
Python from zeroLesson 99: Your first Python programWrite one line, press Run, and watch the computer do exactly what you said. You will also learn to leave notes for humans with comments.Beginner6 min read🧪 Lab📝 Test
Python from zeroLesson 100: Variables, numbers and textWhole numbers, decimal numbers and text are different kinds of values. Learn to use each one, convert between them, and print them neatly with f-strings.Beginner7 min read🧪 Lab📝 Test
Python from zeroLesson 101: Making decisions with ifPrograms become useful when they can choose. Learn to compare values and use if, elif and else to do different things in different cases.Beginner7 min read🧪 Lab📝 Test
Python from zeroLesson 102: Loops: doing things again and againComputers never get bored. Use for, range and while to repeat steps, and to add up or check many values in one go.Beginner7 min read🧪 Lab📝 Test
Python from zeroLesson 103: Lists and dictionariesKeep many values together. A list keeps things in order; a dictionary links each name to a value, like a price list.Beginner7 min read🧪 Lab📝 Test
Python from zeroLesson 104: Functions: reusable stepsA function is a named recipe you can use again and again. Learn def, parameters and return, and why returning beats printing.Beginner5 min read🧪 Lab📝 Test
Python from zeroLesson 105: Reading errors without fearError messages are clues, not punishments. Learn the four errors you will meet most often, and how to read a traceback from the bottom up.Beginner7 min read🧪 Lab📝 Test
Python from zeroLesson 106: Working with data filesMost real data comes as tables: marks lists, timetables, price lists. Learn to read CSV data with Python's csv module and calculate totals and averages from it.Beginner7 min read🧪 Lab📝 Test
Machine learning fundamentalsLesson 107: What machine learning really doesInstead of writing the rule yourself, you give the computer examples and let it find the rule. Learn what that means, and what it does not mean.Middle6 min read🧪 Lab📝 Test
Machine learning fundamentalsLesson 108: Data, features and labelsEvery supervised problem is a table: rows are examples, feature columns are what you know, and the label column is what you want to predict.Middle5 min read🧪 Lab📝 Test
Machine learning fundamentalsLesson 109: Your first prediction: linear regressionFit a straight line to data with scikit-learn, use it to predict numbers, and compare it with a simple baseline so you know if it really helps.Middle5 min read🧪 Lab📝 Test
Machine learning fundamentalsLesson 110: Classification: sorting into groupsLogistic regression predicts which group a row belongs to, and how sure it is. Learn probabilities, thresholds and why scaling features helps.Middle5 min read🧪 Lab📝 Test
Machine learning fundamentalsLesson 111: Decision trees and forestsA decision tree asks a chain of yes/no questions. A random forest asks many slightly different trees and takes a vote.Middle5 min read🧪 Lab📝 Test
Machine learning fundamentalsLesson 112: Nearest neighboursTo label a new example, find the most similar examples you already know and let them vote. Simple, strong, and very sensitive to scale.Middle4 min read🧪 Lab📝 Test
Machine learning fundamentalsLesson 113: Overfitting and how to spot itA model that memorises its training examples looks perfect in practice and fails on new data. Learn to see the gap and to control it.Middle5 min read🧪 Lab📝 Test
Machine learning fundamentalsLesson 114: Train, validation and test setsUse one part of the data to learn, one to choose, and one, touched only once at the end, to report. Plus cross-validation for small datasets.Middle5 min read🧪 Lab📝 Test
Machine learning fundamentalsLesson 115: Measuring a classifier: precision and recallAccuracy can look great while the model misses every case that matters. The confusion matrix, precision and recall show what really happened.Middle5 min read🧪 Lab📝 Test
Machine learning fundamentalsLesson 116: Clustering: finding groups without labelsk-means finds groups of similar rows when nobody has labelled them. Learn how it works, how to pick the number of groups, and why the groups need a human to interpret.Middle5 min read🧪 Lab📝 Test
Machine learning fundamentalsLesson 117: Cleaning messy data with pandasReal tables have missing values, numbers stored as text, spelling variations and duplicate rows. Fix them in a clear order, and keep a record of every change.Middle6 min read🧪 Lab📝 Test
Machine learning fundamentalsLesson 118: Project: predict crop yield from rainfallAn end-to-end project on made-up data: explore, split, set a baseline, try a straight line and a curve, and report the result honestly.Middle6 min read🧪 Lab📝 Test
Capstone projectsLesson 330: Choosing a project that mattersThe best first project solves a small, real problem for people you can actually talk to. Learn how to find one and cut it down to two weeks of work.Middle6 min read🧪 Lab📝 Test
Capstone projectsLesson 331: Plan it in one pageA one-page plan names the problem, the users, the data, how you will measure success, the risks and the timeline. It keeps a group focused and makes the project easy to explain.Middle6 min read🧪 Lab📝 Test
Capstone projectsLesson 332: Build a chatbot for your languageA useful small chatbot answers only from content you trust, cites it, handles how your users really type, and says "not found" instead of guessing.Middle6 min read🧪 Lab📝 Test