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Teacher since May 2025

Kavya Raman

Classical machine learning models, worked through on paper before any code

9

tutors built

4.4Sample

average from 20 reviews

379Sample

lessons taught by their tutors

About Kavya

I teach the classical machine learning models: regression, trees, ensembles, nearest neighbours, support vector machines and naive Bayes. My background is in applied analytics, where I spent a lot of time explaining to colleagues why a model made a particular prediction, and that shaped how I teach. I like to start with a tiny dataset you can hold in your head, work the model through by hand, and only then scale up. I care more about knowing when a model is the wrong choice than about memorising library options, and I try to be honest about what each method cannot do.

Knows about

  • linear and logistic regression
  • decision trees
  • random forests
  • gradient boosting
  • k nearest neighbours
  • support vector machines
  • naive Bayes
  • problem framing

Tutors by Kavya

9 tutors

Linear Regression, Line by Line

Linear Regression, Line by Line

Fit, read and question a linear regression so you know exactly what its numbers meanBeginnerMachine learning4.3(4)80 lessonsSampleKavya Raman$4Framing a Problem for Machine Learning

Framing a Problem for Machine Learning

Turn a vague business wish into a clear prediction task, or learn that it does not need ML at allBeginnerMachine learning4.7(3)60 lessonsSampleKavya RamanFreeGradient Boosting for Tabular Data

Gradient Boosting for Tabular Data

Understand, tune and debug boosted trees, the workhorse of structured data problemsAdvancedMachine learning4.7(3)60 lessonsSampleKavya Raman$12Naive Bayes Classifiers

Naive Bayes Classifiers

Build a fast text classifier from counts and Bayes rule, and know its blind spotsBeginnerMachine learning4.0(3)58 lessonsSampleKavya Raman$3Random Forests and Bagging

Random Forests and Bagging

Understand why averaging many trees works and how to tune a forest sensiblyIntermediateMachine learning4.3(3)54 lessonsSampleKavya Raman$6Support Vector Machines Explained

Support Vector Machines Explained

See margins, support vectors and kernels clearly, then tune C and gamma with confidenceIntermediateMachine learning4.5(2)45 lessonsSampleKavya Raman$7Decision Trees You Can Draw

Decision Trees You Can Draw

Build a decision tree by hand and understand every split it makesBeginnerMachine learning4.5(2)22 lessonsSampleKavya Raman$4k Nearest Neighbours, Built by Hand

k Nearest Neighbours, Built by Hand

Predict by similarity and learn why distance, scaling and k decide everythingBeginnerMachine learningNewKavya Raman$3Logistic Regression for Classification

Logistic Regression for Classification

Model yes or no outcomes, read odds ratios and choose thresholds with intentIntermediateMachine learningNewKavya Raman$6

Recent reviews

What students said about Kavya's tutors.

  • Hamid S.Sample

    I finally understand why my coefficients flipped sign when I added a correlated feature. Good, honest explanations.

    On Linear Regression, Line by Line

  • Yuki T.Sample

    Caught a leak in my target encoding during the categorical lesson. Worth it for that alone.

    On Gradient Boosting for Tabular Data

  • Grace N.Sample

    Clear and unhurried. The one hot encoding lesson was the most useful for me. A little light on how to check residual plots in practice.

    On Linear Regression, Line by Line

  • Erin P.Sample

    Solid basics, but I already knew most of it. Better for true beginners than for people who have run regressions before.

    On Linear Regression, Line by Line

  • Tomasz K.Sample

    The 'say it with units' habit fixed how I present results at work. Moving one point and watching the line tilt made outliers make sense.

    On Linear Regression, Line by Line

  • Nadia F.Sample

    The 'widest street' picture made margins intuitive. The kernel lesson needed two passes but the tutor was patient.

    On Support Vector Machines Explained