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62 Courses

Big Data Scaling in R Using Hadoop and Spark
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Big Data Scaling in R Using Hadoop and Spark

Discussion on wrangling data out of an HDFS and building machine learning models from a large dataset that can then be deployed to an elastically scaled web service for insights.

LIGO – Listening to the Melody of the Universe
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LIGO – Listening to the Melody of the Universe

An introduction to the organizational structure and science behind LIGO detectors, and the data processing techniques that enabled gravitational wave detection.

Sentiment Pipeline for Live Tweets
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Sentiment Pipeline for Live Tweets

An introduction to building a real-time analytics pipeline from sentiment classification model in Azure ML and R. Real-time dashboarding with Power BI and C# using a Twitter dataset.

Completing the Titanic Kaggle Competition in Azure ML
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Completing the Titanic Kaggle Competition in Azure ML

Kaggle is an online community for data scientists that offers machine learning competitions. Complete the Titanic model in Azure ML to get better insights into Kaggle problems and their solutions.

Introduction to Kaggle – My First Kaggle Submission
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Introduction to Kaggle – My First Kaggle Submission

An introduction to Kaggle followed by a guide on how to create a Kaggle account and submit a model to the Kaggle competition. Discussion on typical problems in Kaggle and how to succeed.

Introduction to Recommender Systems
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Introduction to Recommender Systems

A recommender system is an automated system that uses user preference to predict things. A discussion on the importance of recommender systems and how do they work.

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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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.