ML Fundamentals
Classical machine learning is still the backbone of most production AI.
18 lessons · 18 visual stories
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01What Is Machine LearningMachine learning is teaching computers to find patterns in data instead of writing rules by hand.LearnPython45 min▸ Visual story02Linear RegressionLinear regression draws the best straight line through your data. It is the "hello world" of machine learning.BuildPython75 min▸ Visual story03Logistic RegressionLogistic regression bends a straight line into an S-curve to answer yes-or-no questions with probabilities.BuildPython75 min▸ Visual story04Decision Trees and Random ForestsA decision tree is just a flowchart. But a forest of them is one of the most powerful tools in ML.Build—75 min▸ Visual story05Support Vector MachinesFind the widest street between two classes. That is the entire idea.Build—75 min▸ Visual story06K-Nearest Neighbors and DistancesStore everything. Predict by looking at your neighbors. The simplest algorithm that actually works.Build—75 min▸ Visual story07Unsupervised LearningNo labels, no teacher. The algorithm finds structure on its own.BuildPython75 min▸ Visual story08Feature Engineering & SelectionA good feature is worth a thousand data points.BuildPython75 min▸ Visual story09Model EvaluationA model is only as good as the way you measure it.BuildPython75 min▸ Visual story10Bias-Variance TradeoffEvery model error comes from one of three sources: bias, variance, or noise. You can only control the first two.Learn—45 min▸ Visual story11Ensemble MethodsA group of weak learners, combined correctly, becomes a strong learner. This is not a metaphor. It is a theorem.Build—75 min▸ Visual story12Hyperparameter TuningHyperparameters are the knobs you turn before training starts. Turning them well is the difference between a mediocre model and a great one.Build—75 min▸ Visual story13ML PipelinesA model is not a product. A pipeline is. The pipeline is everything from raw data to deployed prediction, and every step must be reproducible.Build—75 min▸ Visual story14Naive BayesThe "naive" assumption is wrong, and it works anyway. That's the beauty of it.Build—75 min▸ Visual story15Time Series FundamentalsPast performance does predict future results -- if you check for stationarity first.Build—45 min▸ Visual story16Anomaly DetectionNormal is easy to define. Abnormal is whatever doesn't fit.Build—75 min▸ Visual story17Handling Imbalanced DataWhen 99% of your data is "normal," accuracy is a lie.Build—75 min▸ Visual story18Feature SelectionMore features is not better. The right features is better.Build—75 min▸ Visual story