What do we call the collection of technologies that make up what we used to call “artificial intelligence?” This conundrum reminds me of a Raymond Carver short story (and book) called What We Talk About When We Talk About Love. Artificial Intelligence (AI) isn’t quite as ambiguous a concept as love, but it’s moving in that […]
A famous dialogue you could listen from the data science people. It could be true if we add it’s so challenging at the end of the dialogue. The foremost challenge starts from categorising the problem itself. The first level of categorising could be whether supervised or unsupervised learning. The next level is what kind of algorithms to get […]
A generalized framework to data-driven consulting projects Overview Here we propose a general framework to solve business problems with data science. This 5-step framework will not only shed light on the subject to someone from the non-technical background, but also allow data enthusiasts to consistently deliver quality results in a timely manner. First we […]
In this blog post, I will discuss feature engineering using the Tidyverse collection of libraries. Feature engineering is crucial for a variety of reasons, and it requires some care to produce any useful outcome. In this post, I will consider a dataset that contains description of crimes in San Francisco between years 2003-2015. The data can be […]
This is the first post in a two-part series that discusses healthcare predictive and propensity modeling and selecting the optimal analytics partner to support your growth and engagement efforts. The second post in this series shares five critical predictive modeling questions to ask a prospective HCRM vendor. When we talk about big data, the numbers can get […]
Articles Overview, goals, learning types, and algorithms Data selection, preparation, and modeling Model evaluation, validation, complexity, and improvement Model performance and error analysis Unsupervised learning, related fields, and machine learning in practice Introduction Welcome to the third article in a five-part series about machine learning. In this article, we’ll continue our machine learning discussion, and […]
Thomas originally posted this article here at http://twiecki.github.io We recently released PyMC3 3.1 after the first stable 3.0 release in January 2017. You can update either via pip install pymc3 or via conda install -c conda-forge pymc3. A lot is happening in PyMC3-land. One thing I am particularily proud of is the developer community we have built. We now have around 10 […]
If you search around the internet looking for applying Naive Bayes classification on text, you’ll find a ton of articles that talk about the intuition behind the algorithm, maybe some slides from a lecture about the math and some notation behind it, and a bunch of articles I’m not going to link here that pretty much just […]
A curated set of resources for artificial intelligence (AI), machine learning, data science, big data, internet of things (IoT), and more. Python Pandas Cheat Sheet – Python for Data Science NumPy Cheat Sheet – Python for Data Science Bokeh Cheat Sheet – DataCamp PySpark Cheat Sheet: Spark in Python R RStudio Cheat Sheet Collection […]
