2016 Aug Tutorials, Overviews

• Learning from Imbalanced Classes - Aug 31, 2016.
  Imbalanced classes can cause trouble for classification. Not all hope is lost, however. Check out this article for methods in which to deal with such a situation.

  Balancing Classes, Bayesian, Learning from Data, Sampling, Tom Fawcett

• How Convolutional Neural Networks Work - Aug 31, 2016.
  Get an overview of what is going on inside convolutional neural networks, and what it is that makes them so effective.

  Brandon Rohrer, Convolutional Neural Networks, Image Recognition, Neural Networks

• What is the Role of the Activation Function in a Neural Network? - Aug 30, 2016.
  Confused as to exactly what the activation function in a neural network does? Read this overview, and check out the handy cheat sheet at the end.

  Linear Regression, Logistic Regression, Neural Networks

• Data Mining Tip: How to Use High-cardinality Attributes in a Predictive Model - Aug 29, 2016.
  High-cardinality nominal attributes can pose an issue for inclusion in predictive models. There exist a few ways to accomplish this, however, which are put forward here.

  Feature Engineering, Feature Selection, Predictive Models

• How Can Data Scientists Mitigate Sensitive Data Exposure Vulnerability? - Aug 26, 2016.
  What is sensitive data? How does it affect data science, and what can be done to mitigate data exposure vulnerability? Read on to find out.

  Security, Sensitive Data

• A Tutorial on the Expectation Maximization (EM) Algorithm - Aug 25, 2016.
  This is a short tutorial on the Expectation Maximization algorithm and how it can be used on estimating parameters for multi-variate data.

  Clustering, Data Science, Data Science Education, Predictive Analytics, Statistics

• Introduction to Local Interpretable Model-Agnostic Explanations (LIME) - Aug 25, 2016.
  Learn about LIME, a technique to explain the predictions of any machine learning classifier.

  Algorithms, Classifier, Explanation, Interpretability, LIME, Machine Learning, Prediction

• A Gentle Introduction to Bloom Filter - Aug 24, 2016.
  The Bloom Filter is a probabilistic data structure which can make a tradeoff between space and false positive rate. Read more, and see an implementation from scratch, in this post.

  Algorithms, Efficiency, Python

• A Primer on Logistic Regression – Part I - Aug 24, 2016.
  Gain an understanding of logistic regression - what it is, and when and how to use it - in this post.

  Classification, CleverTap, Logistic Regression, Regression

• A simple approach to anomaly detection in periodic big data streams - Aug 24, 2016.
  We describe a simple and scaling algorithm that can detect rare and potentially irregular behavior in a time series with periodic patterns. It performs similarly to Twitter's more complex approach.

  Anomaly Detection, Apache Spark, BMW, Time Series, Twitter

• How to Become a (Type A) Data Scientist - Aug 23, 2016.
  This post outlines the difference between a Type A and Type B data scientist, and prescribes a learning path on becoming a Type A.

  Advice, Data Science, Data Scientist, Internet of Things, IoT

• A Neat Trick to Increase Robustness of Regression Models - Aug 22, 2016.
  Read this take on the validity of choosing a different approach to regression modeling. Why isn't L1 norm used more often?

  CleverTap, Linear Regression, Outliers, Overfitting, Regression

• Misinformation Key Terms, Explained - Aug 20, 2016.
  Misinformation has emerged as a key issue for social media platforms. This post will introduce the concept of misinformation and the 8 Key Terms, which provides insights into mining misinformation in social media.

  Explained, Key Terms, Social Media, Social Media Analytics

• The Gentlest Introduction to Tensorflow – Part 2 - Aug 19, 2016.
  Check out the second and final part of this introductory tutorial to TensorFlow.

  Beginners, Deep Learning, Gradient Descent, Machine Learning, TensorFlow

• The 10 Algorithms Machine Learning Engineers Need to Know - Aug 18, 2016.
  Read this introductory list of contemporary machine learning algorithms of importance that every engineer should understand.

  Algorithms, Machine Learning, Supervised Learning, Unsupervised Learning

• Approaching (Almost) Any Machine Learning Problem - Aug 18, 2016.
  If you're looking for an overview of how to approach (almost) any machine learning problem, this is a good place to start. Read on as a Kaggle competition veteran shares his pipelines and approach to problem-solving.

  Advice, Feature Selection, Kaggle, Machine Learning, Modeling

• The Gentlest Introduction to Tensorflow – Part 1 - Aug 17, 2016.
  In this series of articles, we present the gentlest introduction to Tensorflow that starts off by showing how to do linear regression for a single feature problem, and expand from there.

  Beginners, Deep Learning, Gradient Descent, Linear Regression, Machine Learning, TensorFlow

• Central Limit Theorem for Data Science - Aug 12, 2016.
  This post is an introductory explanation of the Central Limit Theorem, and why it is (or should be) of importance to data scientists.

  Centrality, Distribution, Statistics

• Understanding the Empirical Law of Large Numbers and the Gambler’s Fallacy - Aug 12, 2016.
  Law of large numbers is a important concept for practising data scientists. In this post, The empirical law of large numbers is demonstrated via simple simulation approach using the Bernoulli process.

  Algorithms, R, Statistics

• Making Data Science Accessible – Neural Networks - Aug 11, 2016.
  This post attempts to make the underlying concepts of neural networks more accessible to everyone. Gain a high level view of their working here.

  Data Science, Neural Networks

• A Beginner’s Guide to Neural Networks with R! - Aug 11, 2016.
  In this article we will learn how Neural Networks work and how to implement them with the R programming language! We will see how we can easily create Neural Networks with R and even visualize them. Basic understanding of R is necessary to understand this article.

  Beginners, Neural Networks, R, Udemy

• Big Data Key Terms, Explained - Aug 11, 2016.
  Just getting started with Big Data, or looking to iron out the wrinkles in your current understanding? Check out these 20 Big Data-related terms and their concise definitions.

  3Vs of Big Data, Apache Spark, Big Data, Business Intelligence, Cloud Computing, Data Warehouse, Explained, Hadoop, Key Terms, Predictive Analytics

• Exploring Social Media Diversity with Natural Language Processing - Aug 10, 2016.
  This post uses natural language processing on Twitter data to determine the diversity of Twitter accounts the author is following. An innovative take on social media analytics.

  Diversity, Natural Language Processing, NLP, Social Media, Social Media Analytics, Twitter

• 7 Steps to Understanding Computer Vision - Aug 9, 2016.
  A starting point for Computer Vision and how to get going deeper. Dive into this post for some overview of the right resources and a little bit of advice.

  7 Steps, Computer Vision, Deep Learning, Neural Networks, Python

• Brain Monitoring with Kafka, OpenTSDB, and Grafana - Aug 5, 2016.
  Interested in using open source software to monitor brain activity, and control your devices? Sure you are! Read this fantastic post for some insight and direction.

  Brain, Internet of Things, IoT, Kafka, Monitoring

• Contest Winner: Winning the AutoML Challenge with Auto-sklearn - Aug 5, 2016.
  This post is the first place prize recipient in the recent KDnuggets blog contest. Auto-sklearn is an open-source Python tool that automatically determines effective machine learning pipelines for classification and regression datasets. It is built around the successful scikit-learn library and won the recent AutoML challenge.

  Automated, Automated Data Science, Automated Machine Learning, Competition, Hyperparameter, scikit-learn, Weka

• Reinforcement Learning and the Internet of Things - Aug 5, 2016.
  Gain an understanding of how reinforcement learning can be employed in the Internet of Things world.

  Brandon Rohrer, Internet of Things, IoT, Reinforcement Learning, Richard Sutton

• Contest 2nd Place: Automated Data Science and Machine Learning in Digital Advertising - Aug 4, 2016.
  This post is an overview of an automated machine learning system in the digital advertising realm. It is an entrant and second-place recipient in the recent KDnuggets blog contest.

  Advertising, Automated, Automated Data Science, Automated Machine Learning, Claudia Perlich, Machine Learning

• Getting Started with Data Science – R - Aug 3, 2016.
  A great introductory post from DataRobot on getting started with data science in R, including cleaning data and performing predictive modeling.

  Beginners, Data Cleaning, Data Science, Predictive Modeling, R

• Data Science for Beginners: Fantastic Introductory Video Series from Microsoft - Aug 3, 2016.
  The remaining videos in Microsoft's Data Science for Beginners video series are available now. Have a look at what they offer.

  Beginners, Brandon Rohrer, Cortana, Data Science, Microsoft, Microsoft Azure

• Doing Statistics with SQL - Aug 2, 2016.
  This post covers how to perform some basic in-database statistical analysis using SQL.

  SQL, Statistics

• And the Winner is… Stepwise Regression - Aug 1, 2016.
  This post evaluates several methods for automating the feature selection process in large-scale linear regression models and show that for marketing applications the winner is Stepwise regression.

  Automated Data Science, Feature Selection, Linear Regression, Machine Learning, Predictive Analytics