Machine Learning

Downloadable Code Files

Getting Started with Code Files

Getting Started

Introduction

Experiment Goals

Key Members

Product KPI

The North Star Metric

Driver Metric

Guardrail Metrics

Secondary Metrics

Segmentation Metrics

Launch Criteria

Experiment Data

Amazon AB Testing Case - Question

Amazon AB Testing Case - Solution

Code Walkthrough

Understand the Business Problem

State the Experiment Hypothesis

Hypothesis Statement

Significance Level

Statistical Power

Minimum Detectable Effect (MDE)

Design the Experiment

Randomization Unit

Sample Size Calculation

Experiment Duration

Run the Experiment

Experiment Platform

Peeking at P-Values

Assess Validity Threats

Stable Unit Treatment Value Assumption

Survivorship Bias

Sample Ratio Mismatch

Primacy Effect

Novelty Effect

Holiday Effect

Other Effects to Consider

AA Test

Conduct Statistical Inference

Chi-Squared Test of Independence

Z-Test for Proportions

T-Test for Means

Decide Whether to Launch

Interpret Test Results (Relative vs Absolute Difference)

Launch Decisions

Decision Rule

Decision Rule with Confidence Interval

Holdout Set

[Google] An experiment is statistically significant, but the sample is low. Should you change?

[Amazon] An AB test on "frequently bought together" showed improve in test, but not after launch. What happened?

[Amazon] New UI and algorithm are tested on product search. How would you design an experiment?

[Facebook] An experiment on News Feed showed increase in CTR, but the p-value is 0.06. Should you launch the change?

[Instacart] What are important statistical parameters in AB testing?

Interview Cases

[Amazon] Testing Recommender Systems

[Uber] Improving Driver Enrollment Rate

Mock Interviews

Build a solid foundation in machine learning by tackling real interview questions from leading tech companies such as Google, Amazon, Meta, and Uber. Whether you're just starting out or looking to refine your expertise, this course covers essential topics like feature engineering, model evaluation, regularization, and hyperparameter tuning that are frequently asked in data science and ML Engineer interviews. Each module is designed to mirror the structure of interview questions used by top companies, providing you with an insider's view of what to expect during the interview process. With 50+ curated lessons on key machine learning concepts, this course is an invaluable resource for anyone aiming to excel in machine learning interviews at top-tier tech firms.

[Amazon] Neural Network Overfitting

[Google] Normalization

[Google] Optimizers

[Google] RNN vs CNN

[Google] Tuning Dense Neural Networks

[Intuit] Encoder-Decoder

[Microsoft] Backpropagation

[Microsoft] Gradient Descent

[PayPal] Activation Functions

Deep Learning

[Amazon] Bagging vs Boosting

[Amazon] GBTs Hyperparameters

[Amazon] GBTs Pseudocode

[Apple] KNN Pseudocode

[Google] How do you evaluate regression modeling?

[Intuit] Logistic Regression Pseudocode

[Intuit] Variance & Bias in Trees

[Meta] Decision Tree Pseudocode

[Meta] How Is the random forest model trained?

[Microsoft] Finding Optimal K in K-Means

[Microsoft] K-Means

[Microsoft] Principal Component Analysis

[PayPal] Random Forest vs Logistic Regression

[Salesforce] K-Means on Scaled and Unscaled Data

ML Algorithm

[Amazon] Cross-Validation

[Amazon] Euclidean and Manhattan Distances

[Amazon] Handling Categorical Variables

[Amazon] Hyperparameter Tuning

[Amazon] Model Ensembling

[Amazon] The Curse of Dimensionality

[Amazon] Underfitting and Overfitting

[Amazon] Variance-Bias Trade-Off

[C3.ai] Model Building End-To-End

[Capital One] Feature Selection

[Capital One] Handling Missing Values

[Google] Model Evaluation

[Google] Multicollinearity

[Google] Regression Model Metrics

[Meta] Feature Engineering

[Microsoft] Text Features

[PayPal] Imbalanced Labels

[Salesforce] Outliers in Modeling

[Uber] Explain AUC

[Uber] L1 & L2 Regularization

ML Breath

[Capital One] Model Deployment Challenges

[Capital One] Model Failure in Production

[Google] Online vs Offline Model Performance

[Intuit] Kubernetes in Model Deployment

[Intuit] Model Deployment on AWS

[Intuit] Model Orchestration

[PayPal] Prediction Point

ML Production