Discover Python regular expressions: find basic and complex patterns, repetitions, or to do (non-)greedy matching, work with the re library and much more! In this Kaggle tutorial, you'll learn how to approach and build supervised learning models with the help of exploratory data analysis (EDA) on the Titanic data. An introductory tutorial to fast-and-frugal decision trees in R with the FFTrees package. Five useful tips that you can use to effectively improve your R code, from using seq() to create sequences to ditching which() and much more! Learn all about Python dictionary comprehension: how you can use it to create dictionaries, to replace (nested) for loops or lambda functions with map(), filter() and reduce(), ...! Get introduced to Python data structures: learn more about data types and primitive as well as non-primitive data structures, such as strings, lists, stacks, etc. Discover why you should use Amazon Web Services Elastic Compute Cloud (EC2) and how you can set up a basic data science environment on a Windows instance. In this tutorial, you’ll learn how to implement Convolutional Neural Networks (CNNs) in Python with Keras, and how to overcome overfitting with dropout. Discover the R formula and how you can use it in modeling- and graphical functions of well-known packages such as stats, and ggplot2. Learn more about the famous pipe operator %>% and other pipes in R, why and how you should use them and what alternatives you can consider! Learn how to effectively use list comprehension in Python to create lists, to replace (nested) for loops and the map(), filter() and reduce() functions, ...! Learn more about LDA2vec, a model that learns dense word vectors jointly with Dirichlet-distributed latent document-level mixtures of topic vectors. A comprehensive introductory tutorial to Python loops. Learn and practice while and for loops, nested loops, the break and continue keywords, the range function and more! Learn to scrape novels from the web and plot word frequency distributions; You will gain experience with Python packages requests, BeautifulSoup and nltk. In this tutorial, you’ll learn about multi-indices for pandas DataFrames and how they arise naturally from groupby operations on real-world data sets.