Data Science

Basic Time-Series Forecasting

From Machine Learning for Business by Doug Hudgeon and Richard Nichol

This article covers basic time-series forecasting: what it is and why it’s a tough problem.

Function Pipelines for Mapping Complex Transformations

From Mastering Large Datasets with Python by J.T. Wolohan

This article covers

· Using map to do complex data transformations

· Chaining together small functions into pipelines

· Applying these pipelines in parallel on large datasets

Neural Network Architectures

From Probabilistic Deep Learning with Python by Oliver Dürr, Beate Sick, and Elvis Murina

This article dives into neural network architectures and how get started implementing and using them.

The Computer Vision Pipeline, Part 4: feature extraction

From Deep Learning for Vision Systems by Mohamed Elgendy

In this part, we will take a look at feature extraction—a core component of the computer vision pipeline.

Combining Human and Machine Intelligence

From Human-in-the-Loop Machine Learning by Robert (Munro) Monarch

Maximise Customer Retention

From Fighting Churn with Data by Carl Gold

Is my Problem a Graph Problem?

From Graph Databases in Action by Dave Bechberger and Josh Perryman

In this article, we’ll review what makes a problem a good graph use case. We’ll start by examining a few general categories of problems and discussing why they might make for good graph use case.  Finally, we’ll analyze a general framework that we can use to help us decide if our problem is a good graph use case.

The Computer Vision Pipeline, Part 3: image preprocessing

From Deep Learning for Vision Systems by Mohamed Elgendy

In this part, we will delve into image preprocessing for computer vision systems.

The Data Scientist’s Survival Guide

From Build a Career in Data Science by Emily Robinson and Jacqueline Nolis


Want to Learn Machine Learning Inside Out?

From Grokking Machine Learning by Luis G. Serrano


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