Tutorials

Discussion on the steps used in online experimentation. A walkthrough of the overall process, A/A Testing, A/B Testing, Multivariate Testing, and when to use these tests.

Tutorials

Introduction to Natural Language Processing and its key applications. If machines learning from numbers is interesting, then machines learning from words is even more interesting.

Tutorials

An introduction to one-versus-one and one-versus-all classification techniques. Discussion on the differences between the two techniques and when to use one over the other.

Tutorials

Discussion on the basics of A/B testing and answers to in-depth questions such as "Why should businesses conduct A/B testing?" and "How do you perform an A/B test?"

Tutorials

Multivariate testing is a technique used for testing a hypothesis. A discussion on the basics of A/A testing and how to determine which combination of variations performs the best.

Tutorials

A/A testing is a tactic to check that whether the tool being used to run the experiment is statistically fair. A discussion on the basics of A/A testing and how to use them effectively.

Tutorials

An introduction to N-Grams and examples of their usage in Natural Language Processing. The use of N-Grams in training machines to better understand the real meaning of the text.

Tutorials

Time Series is looking at data over time to predict what will happen in the next time period. A discussion on time series, followed by examples of their use in data science.

Tutorials

A brief overview of the confusion matrix, how to create a matrix, and how to use such a matrix. A confusion matrix is one of the basic concepts in data science.

Tutorials

An introduction to the concepts of Precision, Recall, and F1 in data science. The importance of these concepts and when to use the techniques for measuring the accuracy of a model.

Tutorials

An introduction to classification models and their usefulness in the world of data science. When to use classification models in machine learning to make better use of the data.

Tutorials

Azure ML Studio is a fully-featured graphical data science tool that can be used to upload, analyze, clean, and visualize data with an intuitive interface, thus making the tasks easier.

Tutorials

An introduction to the wide world of Big Data and its presence all around us. A bird’s eye view of the subfields of predictive analytics and components of a big data pipeline.

Tutorials

A guide to set up Python and R on a Windows, Mac, or Linux machine followed by a discussion on some common Python and R packages used for machine learning and data analysis.

Tutorials

Web scraping is a very powerful tool for any data science professional. The fundamentals of web scraping, using Python's library (Beautiful Soup) can be of extreme help for a data scientist.