## Tidy Sentiment Analysis in R

Learn how to perform tidy sentiment analysis in R on Prince's songs, sentiment over time, song level sentiment, the impact of bigrams, and much more!## Python Object-Oriented Programming (OOP): Tutorial

Tackle the basics of Object-Oriented Programming (OOP) in Python: explore classes, objects, instance methods, attributes and much more!## K-Means Clustering in R Tutorial

Learn all about clustering and, more specifically, k-means in this R Tutorial, where you'll focus on a case study with Uber data.deep learning

+3## Convolutional Neural Networks with TensorFlow

In this tutorial, you'll learn how to construct and implement Convolutional Neural Networks (CNNs) in Python with the TensorFlow framework.aws

+2## From Local Machine to Dask Cluster with Terraform

Learn how you can take local code that does grid search with the Scikit-Learn package to a cluster of AWS (EC2) nodes with Terraform.r programming

+2## Feature Selection in R with the Boruta R Package

Tackle feature selection in R: explore the Boruta algorithm, a wrapper built around the Random Forest classification algorithm, and its implementation!python

+2## Python XML with ElementTree: Beginner's Guide

Learn how you can parse, explore, modify and populate XML files with the Python ElementTree package, for loops and XPath expressions.facebook live

+4## Experts' Favorite Data Science Techniques

What are the most favorite techniques of the professional data scientists interviewed in DataFramed, a DataCamp podcast? Explore all 6 of them in this tutorial!## Deploy Your Facebook Messenger Bot with Python

A step-by-step guide to easily deploying a Facebook Messenger chatbot with Python, using Flask, requests and ngrok.r programming

+2## Web Scraping in R: rvest Tutorial

Explore web scraping in R with rvest with a real-life project: extract, preprocess and analyze Trustpilot reviews with tidyverse and tidyquant, and much more!statistical modeling

+2## Analyzing Poker Hands with Python

Analyze poker hands with Python and easily implement statistical concepts such as combinations, permutations, (in)dependent events and expected value.machine learning

+1## Ensemble Learning in R with SuperLearner

Boost your machine learning results and discover ensembles in R with the SuperLearner package: learn about the Random Forest algorithm, bagging, and much more!## Generating Realistic Random Datasets with Trumania

Why do data scientists and data engineers work with synthetic data? How do they obtain it? Discover Trumania, a scenario-based random dataset generator library.machine learning

## Active Learning: Curious AI Algorithms

Discover active learning, a case of semi-supervised machine learning: from its definition and its benefits, to applications and modern research into it.blockchain

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