📊 Machine Learning
Get started with Machine Learning
Decision boundaries, model evaluation, and optimization — every core algorithm explained with intuition, not just formulas.
16modules
16quizzes
~2hcontent
Freeto start
What you’ll learn
01🔍 Algorithm intuition
Decision boundaries, explained
Every algorithm — KNN, SVM, decision trees — builds intuition through visuals first.
02📉 Live animation
Gradient descent, animated
Optimization explained with live animations — watch loss fall with each step.
03📐 Model evaluation
Every metric, demystified
Confusion Matrix
TP47
FN3
FP5
TN45
Accuracy94.2%
Precision91.8%
Recall96.5%
F1 Score94.1%
Start the ML Track
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Machine Learning
Learn how machines learn from data — supervised and unsupervised learning, model evaluation, optimisation, and regularisation.
0/16modules·0/16quizzes passed
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Supervised Learning
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Regression
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Classification
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Classification vs Regression
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Unsupervised Learning
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Linear Regression
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The Cost Function
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Gradient Descent
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The Learning Rate
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Regression with Multiple Inputs
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Feature Engineering
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The Bias-Variance Tradeoff
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Overfitting & Regularization
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Regularized Linear Regression
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Classification Metrics
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