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Introduction to Machine Learning

Getting Started

Three kinds of learning, and the two data distinctions that drive almost every later decision.

Level
Beginner
Lessons
2
Total time
30 min
Simulations
2
1 free lessonIncluded in Introduction to Machine Learning · $9
Tools you'll use
  • Python
  • scikit-learn
  • pandas
  • NumPy
Skills
  • Supervised learning
  • Unsupervised learning
  • Data types
Try it now — free

This is the real simulation from the module. No account needed.

What you will be able to do

  • Tell supervised, unsupervised and reinforcement learning apart
  • Classify features as numeric or categorical
  • Frame a business problem as an ML problem

Syllabus

2 lessons · 30 min

Every lesson's title and summary is public. The lessons themselves open once you own the track. The lesson marked free can be read in full right now, no account needed.

Every lesson in Introduction to Machine Learning

  1. Supervised, Unsupervised, Reinforcement

    16 min

    What separates the three is not the algorithm but the shape of the feedback available. One question, and its answer decides everything else.

    FreeRead it free Simulation

  2. Categorical vs Numerical, Structured vs Unstructured

    14 min

    Two distinctions that decide which algorithms are available and what preprocessing is mandatory.

    Included with the track Simulation
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$9
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Before any algorithm. A bad split produces a beautiful number that lies.

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Training, Evaluation, Bias–Variance, Tuning

How to train, how to measure with the right metric, and the one trade-off that explains nearly every model failure.

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KNN — Classifier and Regressor

The algorithm that learns nothing, and teaches a great deal precisely because of that.

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SVM — Classifier and Regressor

Find the boundary with the widest gap to both classes. Simple, and very strong in high dimensions.

  • 1 lesson
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Included in Introduction to Machine Learning· $9