Advanced Learning – Making Knowledge More Accessible

For people who have burning questions, want to solve complex problems, want to learn about an area more efficiently, there is a set of tools, methods, that can open up things like never before.

Data science, and in particular, Machine Learning  provides a way to gain broader, more refined insights.

Data science is, in general terms, the extraction of knowledge from data.  (Wikipedia)

Machine learning is a scientific discipline that deals with the construction and study of algorithms that can learn from data. Such algorithms operate by building a model based on inputs :2 and using that to make predictions or decisions, rather than following only explicitly programmed instructions.  (Wikipedia)

The coolness factor:
Data science and machine learning give the learner the opportunity to gain insights into subjects and problems that traditionally would have taken much longer, and required much manual research.  In addition, machine learning in particular make classifications and even predictions possible that would have been impractical or impossible to do through manual data analysis.
Now data science and even machine learning aren’t exactly new, but more recent advances in tools, techniques, and the easy and affordable availability of computing power, have all converged to open up incredible new possibilities.
There are some very real and exciting things happening, that go way beyond wishful thinking, and are producing solid results.
Deep learning in particular is a promising area, that takes machine learning to a new level.

Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. (deeplearning.net)
 

Deep learning could be seen as neural networks reborn and refined. Neural networks also aren’t new, but new advances and hardware potential have made it a promising area.

Neural Networks
In machine learning and related fields, artificial neural networks (ANNs) are computational models inspired by biological neural networks (the central nervous systems of animals, in particular the brain) and are used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown. Artificial neural networks are generally presented as systems of interconnected “neurons” which can compute values from inputs, and are capable of machine learning as well as pattern recognition thanks to their adaptive nature. (wikipedia)

The uptake of all this is if you are interested in learning about something, or many things in general, 

  • more efficiently 
  • more deeply
  • more broadly

and not spend a half a lifetime coming to useful conclusions you should consider learning about 

  • Computer Programming
    • ​if you want to work with information, computers are simply the best way to do it – a very doable skill to acquire even if you don’t plan on becoming a professional programmer.  There are many libraries where you can take advantage of tools written by high level experts.
      And not just learning any programming language, you should choose one that has good data tools already written.  Python and R are front-runners at the moment for data science, and Javascript is very useful for interactive data visualizations.
  • Data Gathering and Analysis Tools
    • before information can be visualized, it needs to be prepared, features extracted etc. to analyze for patterns.
      We aren’t at a place yet where we can talk to an artificial intelligence, Star Trek like, and get our answers. 
  • Machine Learning
    • This area is the most advanced tool available to extract patterns and meaning from raw data; and with the right visualizations, advanced understanding

​Now of course this represents a significant time investment to acquire these skills.  But until we do have more advanced artificial intelligence, it is the best path to advanced learning.
At least understanding some principles behind methods will be an advantage if you can team up with someone with data science/machine learning skills.
With enough resources of course you can hire talent; but good talent in this area won’t come cheap.  And even if you can afford it, being able to understand and discuss results in a knowledgeable way will be a significant advantage.
 

Feature Selection
In machine learning and statistics, feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features for use in model construction. The central assumption when using a feature selection technique is that the data contains many redundant or irrelevant features. Redundant features are those which provide no more information than the currently selected features, and irrelevant features provide no useful information in any context. (Wikipedia)

All this doesn’t replace being an expert in a particular field; any field that begins to accurately describe reality is going to be complex with lots and lots of details.
But these skills will make answering particular questions, and solving particular problems much more doable for the reasonably educated non-expert.
And will enable you to take advantage of experts’ knowledge, ask the right questions, to gather feature data, data that describes what you are trying to learn about, and build a model that serves you well.

Advanced Learning – Making Knowledge More Accessible

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