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we are now at a critical juncture where many of the systems we need to master are fiendishly complex, from climate change to macroeconomic issues to Alzheimer’s disease. The problem is that these challenges are so complex that even the world’s top scientists, clinicians and engineers can struggle to master all the intricacies necessary to make the breakthroughs required. It has been said that Leonardo da Vinci was perhaps the last person to have lived who understood the entire breadth of knowledge of their age. Since then we’ve had to specialise, and today it takes a lifetime to completely master even a single field such as astrophysics or quantum mechanics. The systems we now seek to understand are underpinned by a vast amount of data, usually highly dynamic, non-linear and with emergent properties that make it incredibly hard to find the structure and connections to reveal the insights hidden therein.  Kepler and Newton could write equations to describe the motion of p...

In  statistics , the  generalized linear model  ( GLM ) is a flexible generalization of ordinary linear regression  that allows for response variables that have other than a  normal distribution . The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a  link function  and by allowing the magnitude of the variance of each measurement to be a function of its predicted value.  in many cases when the response variable must be positive and can vary over a wide scale, constant input changes lead to geometrically varying rather than constantly varying output changes

In  statistics  and, in particular, in the fitting of  linear regression  models, the  elastic net  is a regularized regression method that combines the L1 and L2 penalties of the  lasso  and  ridge  methods.

Lasso  a regularization technique that's useful for feature selection and to prevent over-fitting training data. It works by penalizing the sum of absolute value (L1 norm) of weights found by the  regression.  

Want something that is potentially human comprehensible? Use decision trees or rules. Have a situation where you have lots of memory, but have to learn incrementally and evaluate quickly? Use Nearest Neighbour. Have a clear binary decision in a continuous space? Use SVMs. Have thousands of independent attributes and lots of data? Use Naive Bayes. Have a situation where you know which attributes are correlated with which? Use Bayes nets.

ML algorithms are an evolution over normal algorithms. They make your programs "smarter", by allowing them to automatically learn from the data you provide. You take a randomly selected specimen of mangoes from the market ( training data ), make a table of all the physical characteristics of each mango, like color, size, shape, grown in which part of the country, sold by which vendor, etc ( features ), along with the sweetness, juicyness, ripeness of that mango ( output variables ). You feed this data to the machine learning algorithm ( classification/regressio n ), and it learns a model of the correlation between an average mango's physical characteristics, and its quality.  Next time you go to the market, you measure the characteristics of the mangoes on sale ( test data ), and feed it to the ML algorithm. It will use the model computed earlier to predict which mangoes are sweet, ripe and/or juicy. The algorithm may internally use rules similar to the rules y...

Credit cards with rewards generally target people who spend a lot on credit cards. That high transaction volume is what makes it viable for issuers to provide the rewards. Rewards  are drawn from the in terchange revenue that issuers get from merchants.    To sup port those high transaction volumes, rewards cards generally have a high credit limit. Lower limits do not make economic sense. These cards are generally offered to people with very good credit as a result.    People with no credit history are generally (but not always) considered to be higher risk by issuers, which translates to lower credit limits, which squeezes out rewards propositions.