Tag

data science

Namespacing with Python

From Practices of the Python Pro by Dane Hillard

The article explores the concept of namespaces and how Python uses them to help make code better.

Zero to AI

From Zero to AI by Nicolo Valigi and Gianluca Mauro

Data Science and Bucatini all’Amatriciana

Six Questions for Jesse C. Daniel, author of Data Science with Python and Dask

Jesse Daniel is a software developer (Python, Scala, JavaScript, C#) who leads a team of data scientists at a media technology company. He has taught Python for Data Science at the University of Denver.

Evaluating a Classification Model with a Spam Filter

From Practical Data Science with R, Second Edition by Nina Zumel and John Mount

This article discusses how one can experiment with the effectiveness of a classification model using a spam filter.

Why Choose Azure for Data Engineering?

From Azure Data Engineering by Richard Nuckolls

This article delves into Azure’s tools for data engineering and why you should consider using them.

A New Approach to Deep Learning

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


slideshare-a-new-approach-to-deep-learning

The Computer Vision Pipeline, Part 5: Classifier learning algorithms and conclusion

From Deep Learning for Vision Systems by Mohamed Elgendy

Aggregating Your Data with Spark

From Spark in Action, Second Edition by Jean-Georges Perrin

This article teaches you how to perform an aggregation using Apache Spark. You first look at the definition of an aggregation. You may already know and use aggregations in your job, and this might be a reminder for you. If this is the case, you can safely skim through it: Apache Spark’s aggregations are standard. The second part of this section shows you how to transform a SQL aggregation statement to Spark.

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

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