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Trending Developer Skills, Based on my Analysis of “Ask HN: Who’s Hiring?”

The most popular programming languages, databases and software development tools from "Ask HN: Who is hiring?"
Trending Developer Skills, Based on my Analysis of “Ask HN: Who’s Hiring?”

Machine Learning in Plain English: Building a Decision Tree Model to Classify Names by Gender: Part One

Learn how to build a decision tree model to classify names. by gender, using machine learning, explained in plain English.
Machine Learning in Plain English: Building a Decision Tree Model to Classify Names by Gender: Part One

How I learned R

How I learned R. A little about me and the techniques I used to learn this robust data science language.
How I learned R

New Jupyter Client Released

A new implementation of the Jupyter notebook with realtime synchronization, written using React.js.
New Jupyter Client Released

UC Berkeley Machine Learning Crash Course: Part 1

Learn all the basics of machine learning — regression, cost functions, and gradient descent. This is the first article in Machine Learning at Berkeley's Crash Course series.
UC Berkeley Machine Learning Crash Course: Part 1

Building Data Products with Python: Using Machine Learning to Provide Recommendations

This is the third part of our tutorial on how to build a web-based wine review and recommendation system using Python technologies such as Django, Pandas, SciPy, and Scikit-learn. In this part, you will learn how to use machine-learning to recommend users wines based on their preferences.
Building Data Products with Python: Using Machine Learning to Provide Recommendations

Building Data Products with Python: A Wine Review Website using Django and Bootstrap

With this tutorial, we start a series of tutorials about how to build data products with Python. As a leitmotif we want to build a web-based wine reviews and recommendations website using Python technologies such as Django and Pandas. We have chosen to build a wine reviews and recommendations website, but the concepts and the technology stack...
Building Data Products with Python: A Wine Review Website using Django and Bootstrap

Spark & R: Downloading data and Starting with SparkR using Jupyter notebooks

In this tutorial we will use the 2013 American Community Survey dataset and start up a SparkR cluster using IPython/Jupyter notebooks.
Spark & R: Downloading data and Starting with SparkR using Jupyter notebooks

Building Data Products with Python: Adding User Management to a Django website

This is the second tutorial on our series on how to build data products with Python. In this second tutorial, we will add user management. This is an important part. Once we are able to identify individual users, we will be ready to generate user recommendations through machine learning.
Building Data Products with Python: Adding User Management to a Django website

Data Science with Python & R: Dimensionality Reduction and Clustering

An important step in data analysis is data exploration and representation. In this tutorial we will see how by combining a technique called Principal Component Analysis (PCA) together with Cluster, we can represent in a two-dimensional space data defined in a higher dimensional one while, at the same time, be able to group this data in similar groups or clusters and find hidden relationships in our data.
Data Science with Python & R: Dimensionality Reduction and Clustering

Spark & R: data frame operations with SparkR

In this third tutorial (see the previous one) we will introduce more advanced concepts about SparkSQL with R that you can find in the SparkR documentation, applied to the 2013 American Community Survey housing data. These concepts are related with data frame manipulation, including data slicing, summary statistics, and aggregations.
Spark & R: data frame operations with SparkR

Data Science with Python & R: Exploratory Data Analysis

In this article, we will take a exploratory look at the crucial steps in Python's and R's data analytics process.
Data Science with Python & R: Exploratory Data Analysis

Spark & Python: SQL & DataFrames

This tutorial will introduce you to Spark capabilities. By using SQL language and data frames, you can perform exploratory data analysis easily.

Spark & Python: MLlib Logistic Regression

In this tutorial, you will learn how to use Spark's machine learning library MLlib to build a Logistic Regression classifier for network attack detection.

Spark & Python: Working with RDDs (II)

This is a Spark and Python tutorial that teaches you how to work with RDDs (Part II).

Spark & Python: MLlib Basic Statistics & Exploratory Data Analysis

In this Spark and Python tutorial, you'll learn more about MLlib basic statistics and exploratory data analysis.

Data Science with Python & R: Data Frames II

This is the continued tutorial for learning data science with Python & R. In this part, you will be learning about data selection and function mapping.
Data Science with Python & R: Data Frames II

Spark & Python: Working with RDDs (I)

This tutorial introduces two different ways of getting data into the basic Spark data structure, RDD.

Data Science with Python & R: Data Frames I

These series of tutorials on Data Science will try to compare how different concepts in the discipline can be implemented into the two dominant ecosystems nowadays: R and Python.
Data Science with Python & R: Data Frames I

Building a Movie Recommendation Service with Apache Spark & Flask - Part 1


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