It’s not hard to see machine learning and artificial intelligence in nearly every app we use – from any website we visit, to any mobile device we carry, to any goods or services we use. Where there are commercial applications, data scientists are all over

You may have come across the terms “Precision, Recall and F1” when reading about Classification Models and machine learning. In this Data Science in Minutes tutorial, we will explain what Precision, Recall and F1 are, and when you can use each for measuring the accuracy

Looking at a confusion matrix for the first time can be… confusing. But Data Science Dojo is here to help! In this tutorial, we will give you a brief overview of what a confusion matrix is, how to create your matrix, and when you can

In this video tutorial you will learn how to write standard web scraping commands in R, filter timely data based on time diffs, analyze or summarize key information in the text, and send an email alert of the results of your analysis. Packages used: rvest

Web scraping is a very powerful tool to learn for any data professional. With web scraping, the entire internet becomes your database. In this Introduction to Web Scraping with Python and Beautiful Soup tutorial, we show you how to parse a web page into a

R is currently one of the most popular data science languages in the world. However, it’s always had constraints around scaling out to big data. What happens when you expand beyond a couple gigabytes of data? You packed up your data and you used something

R programming is rapidly becoming a valuable skill for data professionals of all stripes and a must-have skill for aspiring data scientists. Adding R programming to your data analyst skillset allows you to leverage powerful data visualizations, statistical analyses, and even machine learning in your

This video concludes our Introduction to Text Analytics with R and covers: – Optimizing our model for the best generalizability on new/unseen data. – Discussion of the sensitivity/specificity tradeoff of our optimized model. – Potential next steps regarding feature engineering and algorithm selection for additional

Your First Test includes specific coverage of: – Pre-processing new, unseen textual data to allow for predictions from our trained model. – The importance of caching the IDF values calculated from the training data set to TF-IDF new, unseen, pre-processed data. – Performing SVD projections

Cosine Similarity includes specific coverage of: – How cosine similarity is used to measure similarity between documents in vector space. – The mathematics behind cosine similarity. – Using cosine similarity in text analytics feature engineering. – Evaluation of the effectiveness of the cosine similarity feature.

Model Metrics includes specific coverage of: – The importance of metrics beyond accuracy for building effective models. – Coverage of sensitivity and specificity and their importance for building effective binary classification models. – The importance of feature engineering for building the most effective models. –

SVD with R includes specific coverage of: – Use of the irlba package to perform truncated SVD. – How to project a TF-IDF document vector into the SVD semantic space . – Comparison of model performance between a single decision tree and the mighty random

The R programming language is experiencing rapid increases in popularity and wide adoption across industries. This popularity is due, in part, to R’s huge collection of open source machine learning algorithms. If you are a data scientist working with R, the caret package is a

Microsoft’s Power BI is a powerful technology for quickly creating rich visualizations. Power BI has many practical uses for the modern data professional including executive dashboards, operational dashboards, and visualizations for data exploration/analysis. Microsoft has also extended Power BI with support for incorporating R visualizations

The R programming language is experiencing rapid increases in popularity and wide adoption across industries. This popularity is due, in part, to R’s rich and powerful data visualization capabilities. While tools like Excel, Power BI, and Tableau are often the go-to solutions for data visualizations,

In this final tutorial of our Introduction to dplyr series, we will cover ways to do feature engineering both with dplyr and base R . You’ll learn how to impute missing values as well as create new values based on existing columns. In addition, we’ll

We introduce functions that make it easy to find overlapping and distinct values from two different data sources, intersect and setdiff. These two functions let you see the shared and unique elements from different vectors, making it easy to spot commonalities and differences. After watching

We go over some basic functions of dplyr including the mighty group_by and summarize combo that makes dividing up datasets a breeze, as well as arrange, select, and filter that help get the data in a cleaner and more organized format. Group-by aggregation is one

dplyr is a a great tool to perform data manipulation. It makes your data analysis process a lot more efficient. Even better, it’s fairly simple to learn and start applying immediately to your work! Oftentimes, with just a few elegant lines of code, your data

dplyr is a a great tool to perform data manipulation. It makes your data analysis process a lot more efficient. Even better, it’s fairly simple to learn and start applying immediately to your work! Oftentimes, with just a few elegant lines of code, your data

Modern machine learning libraries make model building look deceptively easy. An unnecessary emphasis on tools like R, Python, SparkML, and techniques like deep learning is prevalent. Relying on tools and techniques while ignoring the fundamentals is the wrong approach to model building. Real-world machine learning

In this meetup, we provide a quick introduction to online experimentation and A/B testing. To keep the tutorial self-contained, we will first give an overview of stats fundamentals needed to understand A/B testing. We then explain how A/B testing is done in an online business.

The R programming language is experiencing rapid increases in popularity and wide adoption across industries. This popularity is due, in part, to R’s rich and powerful data visualization capabilities. While tools like Excel, Power BI, and Tableau are often the go-to solutions for data visualizations,

Ever wonder what classification models are? Well, in machine learning there are many different models, all with different types of outcomes. In this quick tutorial, we go over classifications models. We talk about what they are, as well as what they are used for.

In this tutorial we go over the steps you need to take for online experimentation. When running a business, there are many different moving parts happening all at once. This is why it is helpful to carefully draw out your steps in experimentation. We go

So, we’ve just covered what is an A/B test and what is a multivariate test, but what is an A/A test? In this quick tutorial we go over A/A testing, what it is and how to use it to help you properly conduct A/B or

What is A/B testing? In this quick tutorial, we go over the basics of A/B testing, as well as answer some in-depth questions such as: why should businesses conduct A/B testing? Or how do you perform an A/B test?

In this tutorial, we will explain: how a multivariate test differs from an A/B Test, how to create and conduct a multivariate test, and what questions you should be asking of your test. Multivariate testing is a technique for testing a hypothesis in which multiple

Introduction to Azure ML – Let’s build our first machine learning model in Azure ML. First, we have to go shopping for a machine learning model. We must identify what type of machine learning algorithm we want to choose from. We ended up using a

Introduction to Azure ML – In this tutorial we will make sure all the categorical features are treated as categories using the edit meta data module. We will also setup a holdout dataset and randomly sample our dataset in two partitions, a training set and

Introduction to Azure ML – Let’s understand the aggregate behavior of our features further by looking at summary statistics. Azure Machine Learning gives us easy access to mean, median, mode, min, and max. Let’s look at each measure to see what it means to the

Introduction to Azure ML – The machine learning model will learn from the data it has access to. Sometimes it becomes necessary to shed columns from our dataset so our machine learning model does not learn from them. In this video we’ll drop a few

Introduction to Azure ML – In the last video, we prepared our dataset for a join. In this video we’ll use the join data module inside of Azure ML to cross reference each airport id with the airport table to find the airport’s city, state

Introduction to Azure ML – Now that we have a better understanding of our dataset. Let’s go out and gather more data. There is an additional dataset inside of Azure ML we can use to look up and cross reference the airport name with airport

Introduction to Azure ML – Now that we have Azure Machine Learning Studio setup, let’s begin an end-to-end data science project in Azure Machine Learning. We’ll choose the flight delay data, and use it to predict whether not a flight will be late on arrival

Introduction to Azure ML – Today we’ll explore the interface of our new machine learning tool, Azure ML. How do you bring data to and from the outside world into Azure ML? The import dataset module can read in data from a variety of sources:

Learn the basics of natural language processing: the components of NLP , enterprise applications of NLP, and finally build a simple FAQ Chatbot. About the Speaker: Chris Shei is the technical evangelist for Jet.com where he explores trending tech and helps Jet’s engineering org build

Introduction to Azure ML – To begin data mining in Azure Machine Learning Studio, we must first setup an Azure subscription. Once we have a subscription, we can create an Azure Machine Learning Studio workspace within Azure. Azure Machine Learning is in the cloud and

The overview of this video series provides an introduction to text analytics as a whole and what is to be expected throughout the instruction. It also includes specific coverage of: – Overview of the spam dataset used throughout the series – Loading the data and

Part 7 of this video series includes specific coverage of: – The trade-offs of expanding the text analytics feature space with n-grams. – How bag-of-words representations map to the vector space model . – Usage of the dot product between document vectors as a proxy

In our next installment of introduction to text analytics, data pipelines, we take cover: – Exploration of textual data for pre-processing “gotchas” – Using the quanteda package for text analytics – Creation of a prototypical text analytics pre-processing pipeline, including : tokenization, lower casing, stop

N-grams includes specific coverage of: • Validate the effectiveness of TF-IDF in improving model accuracy. • Introduce the concept of N-grams as an extension to the bag-of-words model to allow for word ordering. • Discuss the trade-offs involved of N-grams and how Text Analytics suffers

Introduction to Azure ML – What’s better than machine learning? Machine learning where coding is optional! Drag and drop machine learning with a visual interface! We’re going to introduce you to a new tool to add to your data science toolkit, Azure Machine Learning Studio.

In this 90-minute video tutorial, we will cover an overview of solving a simple predictive analytics problem. We will use R for Feature Exploration, Visualization, and Predictive Modeling with R and Azure ML We will be using the Titanic data set for our exercise. You

At this Data Science Dojo meetup, Phuc Duong talks about Building a Real-Time Sentiment Pipeline for Live Tweets Using Python, R, & Azure Supplementary Material found here: https://github.com/gokul180288/meetup

At this meetup, presenter Craig Guarraci speaks about how to Make Sense of Unstructured Text With Python, MS Cognitive Services & PowerBI – In this presentation we’ll take a broad look at industry research to see how text analytics and sentiment analysis is used –

Nearly 100 years after Einstein predicted the existence of gravitational waves, Laser Interferometer and Gravitational Wave Observatory astounded the world by successfully detecting these waves. Detection was made possible by the advancement of laser technology and data processing techniques. Being able to distinguish the gravitational

Event logs are everywhere and represent a prime source of Big Data. Event log sources run the gamut from e-commerce web servers to devices participating in globally distributed Internet of Things architectures. Even Enterprise Resource Planning systems produce event logs! Given the rich and varied

It’s not hard to see machine learning and artificial intelligence in nearly every app we use – from any website we visit, to any mobile device we carry, to any goods or services we use. Where there are commercial applications, data scientists are all over

Learn the basics of natural language processing: the components of NLP , enterprise applications of NLP, and finally build a simple FAQ Chatbot. About the Speaker: Chris Shei is the technical evangelist for Jet.com where he explores trending tech and helps Jet’s engineering org build

The R programming language is experiencing rapid increases in popularity and wide adoption across industries. This popularity is due, in part, to R’s rich and powerful data visualization capabilities. While tools like Excel, Power BI, and Tableau are often the go-to solutions for data visualizations,

dplyr is a a great tool to perform data manipulation. It makes your data analysis process a lot more efficient. Even better, it’s fairly simple to learn and start applying immediately to your work! Oftentimes, with just a few elegant lines of code, your data

This presentation will discuss building a business case for your machine learning idea. In this talk, our presenter, Neeti Gupta, will provide a 10-step checklist with examples for the audience to build their own business model. This 10-step business checklist is a synthesis of the

From distorting experiments with systemic bias to imposing human ethics on machine learning models, data scientists have far more to worry about than the raw numbers in their spreadsheet. Join Raja Iqbal on an exploration of data science’s past evils and how we can pave

According to some estimates, bots constitute close to 50% of the overall traffic. In this introductory talk to Feature Engineering for Bot Detection, we will cover various aspects of feature engineering for bot detection of automated web traffic. We will start with understanding the impact

In this meetup, we provide a quick introduction to online experimentation and A/B testing. To keep the tutorial self-contained, we will first give an overview of stats fundamentals needed to understand A/B testing. We then explain how A/B testing is done in an online business.

Modern machine learning libraries make model building look deceptively easy. An unnecessary emphasis on tools like R, Python, SparkML, and techniques like deep learning is prevalent. Relying on tools and techniques while ignoring the fundamentals is the wrong approach to model building. Real-world machine learning

The R programming language is experiencing rapid increases in popularity and wide adoption across industries. This popularity is due, in part, to R’s rich and powerful data visualization capabilities. While tools like Excel, Power BI, and Tableau are often the go-to solutions for data visualizations,

Storytelling is a cornerstone of the human experience. Though many elements of stories have remained the same throughout history, we have developed better tools and mediums for telling them, such as printed books, movies, and comics. This has changed storytelling styles—and perhaps most importantly, the

The R programming language is experiencing rapid increases in popularity and wide adoption across industries. This popularity is due, in part, to R’s huge collection of open source machine learning algorithms. If you are a data scientist working with R, the caret package is a

Lecture Starts at: 8:25 Business data presents a challenge for the data analyst. Business data is often aggregated, recorded over time, and tends to exhibit autocorrelation. Additionally, and most problematically, the amount of business data is usually quite limited. These characteristics lead to a situation

R programming is rapidly becoming a valuable skill for data professionals of all stripes and a must-have skill for aspiring data scientists. Adding R programming to your data analyst skillset allows you to leverage powerful data visualizations, statistical analyses, and even machine learning in your

Event logs are everywhere and represent a prime source of Big Data. Event log sources run the gamut from e-commerce web servers to devices participating in globally distributed Internet of Things architectures. Even Enterprise Resource Planning systems produce event logs! Given the rich and varied

Microsoft’s Power BI is a powerful technology for quickly creating rich visualizations. Power BI has many practical uses for the modern data professional including executive dashboards, operational dashboards, and visualizations for data exploration/analysis. Microsoft has also extended Power BI with support for incorporating R visualizations

R is currently one of the most popular data science languages in the world. However, it’s always had constraints around scaling out to big data. What happens when you expand beyond a couple gigabytes of data? You packed up your data and you used something

Nearly 100 years after Einstein predicted the existence of gravitational waves, Laser Interferometer and Gravitational Wave Observatory astounded the world by successfully detecting these waves. Detection was made possible by the advancement of laser technology and data processing techniques. Being able to distinguish the gravitational

At this meetup, presenter Craig Guarraci speaks about how to Make Sense of Unstructured Text With Python, MS Cognitive Services & PowerBI – In this presentation we’ll take a broad look at industry research to see how text analytics and sentiment analysis is used –

At this Data Science Dojo meetup, Phuc Duong talks about Building a Real-Time Sentiment Pipeline for Live Tweets Using Python, R, & Azure Supplementary Material found here: https://github.com/gokul180288/meetup

In this 90-minute video tutorial, we will cover an overview of solving a simple predictive analytics problem. We will use R for Feature Exploration, Visualization, and Predictive Modeling with R and Azure ML We will be using the Titanic data set for our exercise. You