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R Learning Path: From beginner to expert in R in 7 steps
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Time Series Analysis: A Primer - Jan 17, 2017.
Time series analysis is a complex subject but, in short, when we use our usual cross-sectional techniques such as regression on time series data, variables can appear "more significant" than they really are and we are not taking advantage of the information the serial correlation in the data provides.
90 Active Blogs on Analytics, Big Data, Data Mining, Data Science, Machine Learning (updated) - Jan 17, 2017.
Stay up-to-date in the data science with active blogs. This is a list of 90 recently active blogs on Big Data, Data Science, Data Mining, Machine Learning, and Artificial intelligence.
Introduction to Forecasting with ARIMA in R - Jan 16, 2017.
ARIMA models are a popular and flexible class of forecasting model that utilize historical information to make predictions. In this tutorial, we walk through an example of examining time series for demand at a bike-sharing service, fitting an ARIMA model, and creating a basic forecast.
A Concise Overview of Recent Advances in Chatbot Technologies - Jan 13, 2017.
2016 saw some big leaps in chatbot technologies (along with a few unforeseen embarrassments). Get a quick review of the big events in the space over the past year, complete with supporting videos.
A Concise Overview of Recent Advances in the Internet of Things (IoT) - Jan 12, 2017.
A lot happened in IoT during 2016. Read this post for a briefing on some of the most important events, how they unfolded, and what they mean moving forward, complete with select videos to reinforce and elaborate.
A Concise Overview of Recent Advances in Vehicle Technologies - Jan 11, 2017.
2016 was a big year for electric and driverless cars. Get a quick review with relevant videos on some of the events of interest in the field during the past year.
Internet of Things Tutorial: WSN and RFID – The Forerunners - Jan 6, 2017.
WSN and RFID are key to understanding more complex IoT concepts and technologies, but also the structure of non-trivial IoT systems, which are very likely to comprise RFID or WSN components.
Sound Data Science: Avoiding the Most Pernicious Prediction Pitfall - Jan 5, 2017.
Data science and predictive analytics can provide huge value, but they can mislead and backfire if not used with fail-safe measures. The author gives examples of such problems and provides guidelines to avoid them.
Creating Data Visualization in Matplotlib - Jan 5, 2017.
Matplotlib is the most widely used data visualization library for Python; it's very powerful, but with a steep learning curve. This overview covers a selection of plots useful for a wide range of data analysis problems and discusses how to best deploy each one so you can tell your data story.
Tidying Data in Python - Jan 4, 2017.
This post summarizes some tidying examples Hadley Wickham used in his 2014 paper on Tidy Data in R, but will demonstrate how to do so using the Python pandas library.
Generative Adversarial Networks – Hot Topic in Machine Learning - Jan 3, 2017.
What is Generative Adversarial Networks (GAN) ? A very illustrative explanation of GAN is presented here with simple examples like predicting next frame in video sequence or predicting next word while typing in google search.
3 methods to deal with outliers - Jan 3, 2017.
In both statistics and machine learning, outlier detection is important for building an accurate model to get good results. Here three methods are discussed to detect outliers or anomalous data instances.
Machine Learning and Cyber Security Resources - Jan 2, 2017.
An overview of useful resources about applications of machine learning and data mining in cyber security, including important websites, papers, books, tutorials, courses, and more.
- The big data ecosystem for science: Climate Science and Climate Change
4 Reasons Your Machine Learning Model is Wrong (and How to Fix It)
This post presents some common scenarios where a seemingly good machine learning model may still be wrong, along with a discussion of how how to evaluate these issues by assessing metrics of bias vs. variance and precision vs. recall.
- Data Science Basics: Power Laws and Distributions
- ResNets, HighwayNets, and DenseNets, Oh My!
- Introduction to Bayesian Inference
- The big data ecosystem for science: Genomics
50+ Data Science, Machine Learning Cheat Sheets, updated
Gear up to speed and have concepts and commands handy in Data Science, Data Mining, and Machine learning algorithms with these cheat sheets covering R, Python, Django, MySQL, SQL, Hadoop, Apache Spark, Matlab, and Java.
- The Costs of Misclassifications
- Data Science Basics: What Types of Patterns Can Be Mined From Data?
- Data Analytics Models in Quantitative Finance and Risk Management
- Data Science and Big Data: Definitions and Common Myths
- Introduction to K-means Clustering: A Tutorial
- Artificial Neural Networks (ANN) Introduction, Part 2
- Artificial Neural Networks (ANN) Introduction, Part 1
- The Best Metric to Measure Accuracy of Classification Models
- Internet of Things Tutorial: Introduction
- Random Forests in Python
- Data Science Deployments With Docker