What is the Data Science Life Cycle?

I describe the data science life cycle, a methodology for effectively developing and deploying data-driven projects.

A Complete Machine Learning Project From Scratch: Model Deployment and Continuous Integration

In this fifth post in a series on how to build a complete machine learning product from scratch, I describe how to deploy our model and set up a continuous integration system.

A Complete Machine Learning Project From Scratch: Error Analysis And Model V2

In this fourth post in a series on how to build a complete machine learning product from scratch, I describe how to error analyze our first model and work toward building a V2 model.

A Complete Machine Learning Project From Scratch: Model V1

In this third post in a series on how to build a complete machine learning product from scratch, I describe how to build an initial model with an associated training/evaluation pipeline and functionality tests.

A Complete Machine Learning Project From Scratch: Exploratory Data Analysis

In this second post in a series on how to build a complete machine learning product from scratch, I describe how to acquire your dataset and perform initial exploratory data analysis.

A Complete Machine Learning Project From Scratch: Setting Up

In this first post in a series on how to build a complete machine learning product from scratch, I describe how to setup your project and tooling.

We Don't Need Data Scientists, We Need Data Engineers

After analyzing 1000+ Y-Combinator Companies, I discover there's a huge market need for more engineering-focused data practitioner roles.

A Comprehensive Introduction to Machine Learning Fundamentals

I provide an aggregated and comprehensive list of tutorials I have made for fundamental concepts in machine learning.

Market Basket Analysis

I discuss market basket analysis, an unsupervised learning technique for understanding and quantifying the relationships between sets of items.

Decision Trees

I discuss decision trees which are very powerful, general-purpose models that are also interpretable.

Your Closest Neighbors

I discuss the k-nearest neighbors algorithm, a remarkably simple but effective machine learning model.

Model Evaluation

In which I describe commonly used techniques for evaluating machine learning models.

Join the Ensemble

In which I discuss the technique of ensembling which is used to improve the performance of a single machine learning model by combining the power of several other models.

Why Did You Choose This Model?

I discuss strategies such as cross-validation which are used for selecting best-performing machine learning models.

Principal Components Analysis

In which I give a primer on principal components analysis, a commonly used technique for dimensionality reduction in machine learning.

Model Regularization

In which I discuss regularization, a strategy for controlling a model's generalizability to new datasets.

Controlling Your Model's Bias

In which I describe the bias-variance tradeoff, one of the most important concepts underlying all of machine learning theory.

What Features Do You Want?

In which I discuss feature selection which is used in machine learning to help improve model generalization, reduce feature dimensionality, and do other useful things.

What K-Means

In which we investigate K-means clustering, a common unsupervised clustering technique for analyzing data.

Basics of Support Vector Machines

We discuss support vector machines, a very powerful and versatile machine learning model.

Naive Bayes Classifier Tutorial

I describe Naive Bayes, a commonly-used generative model for a variety of classification tasks.

Logistic Regression in Machine Learning Tutorial

I describe logistic-regression, one of the cornerstone algorithms of the modern-day machine learning toolkit.

Fundamentals of Linear Regression

I describe the basics of linear regression, one of the most common and widely used machine learning techniques.

Being a Good Machine Learning Engineer/Data Scientist

A discussion of the most important skills necessary for being an effective machine learning engineer or data scientist.