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- Machine Learning for Data Analysis
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- Data Analysis, Machine Learning and Knowledge Discovery
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After training an ML model on the inputs, the computer has hopefully "learned" something about the connections between these millions of inputs, and can hopefully make predictions regarding new unseen inputs. For one example, take a look at the research areas of the faculty in Carnegie Mellon's Machine Learning Department.
For another example, all three of the authors of Elements of Statistical Learning — which seems to be one of the standard textbooks in machine learning — are statisticians. On the other hand, the term "data analysis" is used in radically different ways in different sectors.
The data analysts in a biology lab might be a couple postdocs and grad students who have taught themselves enough R or Python to run statistical tests and generate plots.
Data journalists at news media outlets might focus more on building visualizations and interactives of data aggregates and summaries using D3, with little or no deep quantitative analysis i. Because "data analysis" has different aims and goals in different sectors — and is done by people with very different kinds of formal training — different kinds of "data analysts" will use different software tools and different methods.
Machine Learning for Data Analysis
Deep learning and other predictive models will be appropriate for some kinds of data analysis web- and app-based companies; certain data-cleaning purposes in scientific research but irrelevant for others data journalism.
Data analysis usefully defined, via wikipedia , emphasis mine:.
Data analysis, also known as analysis of data or data analytics, is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.
Meaning, the focus is on deriving information, insight, or conclusions so that humans may do, understand or decide better. Tom Mitchell wrote a very helpful definition of in his book Machine Learning , as quoted on wikipedia :.
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A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. Here, the emphasis is on programming agents that learn to do things—predict housing prices, transcribe text—from experience, i.
This, I find, is a helpful distinction: Instead of method, we can reason about goals. If we're analyzing Fourier coefficients of recorded speech to understand how the human brain parses sounds into vowels, that's analysis; if we're doing to write a program that automatically transcribes text, it's machine learning.
What is an insightful KPI to get out of our data? In some deeper level, data analysis you are typically using the machine to automate some fairly trivial tasks.
With machine learning, you are letting the machine automate a lot of the learning process - where you would be typically passing in that knowledge as a data analyst. Data Analysis is a process of understanding the data, find patterns and try to obtain inferences due to which the underlying patterns are observed.
Machine Learning is when you train a system to learn those patterns and try to predict the upcoming pattern. Think of Amazon. The employee then decides to move all the chocolate desks to the supermarket entrance in order to increase the probability and also attract more customers to buy chocolates.
Here observing the pattern and behavior of shopping by the customers is data analysis, learning similar patterns and changing the desk place to increase those probabilities is the machine learning process.
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Dan Hicks Dan Hicks 3 3 bronze badges. Tom Mitchell wrote a very helpful definition of in his book Machine Learning , as quoted on wikipedia : A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.
Very diverse field, little consensus re terms, your mileage may vary. Sean Easter Sean Easter 8 8 bronze badges. Machine learning, is a whole different game.
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You might re-state Q1 and Q2, for example: Can we build a predictive algorithm that predicts what pages will be out of stock? Can we build an optimizer which helps us to dispatch orders faster? Does that help clarify at all? Toros91 2, 2 2 gold badges 10 10 silver badges 29 29 bronze badges. Henry Henry 2 2 bronze badges.
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Data Analysis, Machine Learning and Knowledge Discovery
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