Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
Visualizing Decision Trees with Pybaobabdt
 Originally published in Towards Data Science, Dec 14, 2021....
Correspondence Analysis: From Raw Data to Visualizing Relationships
 Isn’t it satisfying to find a tool that makes...
Podcast: Four Things the Machine Learning Industry Must Learn from Self-Driving Cars
    Welcome to the next episode of The Machine...
A Refresher on Continuous Versus Discrete Input Variables
 How many times have I heard that the most...

Industry News

Why not now? The barriers to adopting true predictive analytics.

 While reading Jared Diamond’s excellent book on the rise and subsequent global dominance of Eurasian societies Guns, Germs and Steel, I was stopped in my tracks by his chapter on the evolution of technology entitled Necessity’s Mother. Diamond briskly demolishes the commonly-held view that necessity is the mother of invention. In fact he argues that many

2014 Big Data Predictions from IDC and IIA

 Both IDC and The International Institute of Analytics (IIA) discussed their big data and analytics predictions for 2014 in separate webcasts last week. Here is my summary of their predictions plus a few nuggets from other sources. IDC predicts...

Hiring 1 Data Science unicorn is hard enough, a team is impossible. To scale means to specialise.

 The Data Scientists need a large set of skills, including business know-how, modelling and mathematics, plus programming. They are as hard to find as unicorns, or superheroes. I know this talent shortage first hand. Is the solution...

Python Displacing R As The Programming Language For Data Science

  R remains popular with the PhDs of data science, but as data moves mainstream, Python is taking over. While R has traditionally been the programming language of choice for data scientists, it is quickly ceding ground...

Understanding Your Business With Descriptive, Predictive And Prescriptive Analytics

 Companies have long been involved in the analysis of how a company performed over time. As the history of big data shows, already for many years we try to understand how the organisations or the world around...

“The hungry statistician” – or why we never can get enough data

 As the “Year of Statistics” comes to a close, I write this blog in support of the many statisticians who carefully fulfil their analysis tasks day by day, and to defend what may appear to be demanding behavior when...

Analytics 3.0 — the old guard masters how to build data products

 Asked to name a big data company, many of us would say Google or Facebook or eBay. But for old-school giants such as General Electric Co. and Macy’s Inc., big data is fast becoming as central to...

Data Science Wars: Python vs. R

 As I frequently travel in data science circles, I’m hearing more and more about a new kind of tech war: Python vs. R. I’ve lived through many tech wars in the past, e.g. Windows vs. Linux, iPhone...

Rexer Analytics 2013 Data Miner Survey Highlights

 Top 5 most used tools were R (used by 70% of data miners), IBM SPSS Statistics, Rapid Miner, SAS, and Weka, while STATISTICA, KNIME, SAS JMP, IBM SPSS Modeler, and RapidMiner had the the highest satisfaction. Big...

Predictive Analytics, a game changer for transaction banking

 Transaction banking has made huge strides in recent years. The easy availability of new technologies has improved operating efficiencies and accuracy. Apart from this, the speed of transactions has also increased remarkably. The volume of transaction banking...

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