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56 Courses

Clustering Introduction
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Clustering Introduction

Introduction to the technique of clustering and the fundamental concepts associated with it. Discussion on clustering methods along with their strengths and weaknesses.

Steps in Experimentation
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Steps in Experimentation

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.

Natural Language Processing
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Natural Language Processing

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.

One Versus One vs. One Versus All in Classification Models
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One Versus One vs. One Versus All in Classification Models

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.

A/B Testing in Minutes
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A/B Testing in Minutes

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?"

Multivariate Testing in Minutes
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Multivariate Testing in Minutes

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.

A/A Testing in Minutes
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A/A Testing in Minutes

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.

N-Grams in Minutes
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N-Grams in Minutes

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.

Time Series Forecasting in Minutes
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Time Series Forecasting in Minutes

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.

Introduction to the Confusion Matrix
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Introduction to the Confusion Matrix

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.

Introduction to Precision, Recall, and F1 in Classification Models
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Introduction to Precision, Recall, and F1 in Classification Models

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.

Introduction to Classification Models
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Tutorials

Introduction to Classification Models

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.

Introduction to Azure ML and Cloud Computing
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Introduction to Azure ML and Cloud Computing

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.

Introduction to Big Data, Data Science and Predictive Analytics
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Introduction to Big Data, Data Science and Predictive Analytics

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.

Getting started with Python and R for Data Science
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Getting started with Python and R for Data Science

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.

Introduction to Web Scraping Using Python and Beautiful Soup
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Introduction to Web Scraping Using Python and Beautiful Soup

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.