Why A.I and machine learning is the key to unlocking creativity and productivity

Why A.I and #machinelearning is the key to unlocking creativity and productivity

  • A few weeks ago, the Royal Society of Machine Learning released a report considering the state of Machine Learning in the UK.
  • As with many reports and studies around the ‘rise of the machine learning’ era the Royal Society of Machine Learning’s report shouts about the potential that A.I solutions can provide to every corner of our Society, it also highlights legitimate concerns from the same societies that A.I technology is meant…
  • The report references a survey conducted by the Society, which shows that there is an overriding concern, also aired in the national press, that jobs will be stolen by the machine, big businesses will increase profits and the unemployment lines will grow.
  • The survey also showed that there is a real concern that skill levels will be eroded and that there should be a focus on up-skilling in areas of data science and machine learning, to provide a career path to those who will be displaced by the application of these solutions….
  • Teams that are impacted using machine learning and A.I can now focus on growing their business and personal success.

A few weeks ago, the Royal Society of Machine Learning released a report considering the state of Machine Learning in the UK. It contained a lot of valuable information on the history, current implementations and future uses of A.I – I wanted to share my highlights of that report and investigate what it means for businesses.
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A Brief Primer on Linear Regression – Part 1

A Brief Primer on Linear Regression – Part 1  #MachineLearning

  • Some examples could be

    These relationships, not of perfect kind nature, when graphed, gives a scatter plot of points, as seen from the plot of height-weight information of 30 adults as below:

    The above scatter plot reflects the relationship between height and weight as linear, depicting a positive increase in weight with height.

  • The data points in the above scatter plot could be summarized in many ways as shown by various lines in the below plot:

    Now the question arises – What is the best fit line that summarizes the relationship between height-weight, amongst all possible lines?

  • The best fit line is the one in blue color, and termed as regression line, which is actually the plot of the predicted score on y, for each possible value of x.

    But, the next question comes – how to arrive at this best line?

  • The best line fitting the given data is obtained by “minimizing the residual variation” as below

    is the actual observed value of response variable,

    is the predicted value of response variable (as obtained from the model), and

    is the residual variation – the variation between the observed and predicted value of y

    The closer the regression line comes to all the data points on the scatter plot, the better it is.

  • The best fit straight line to summarize the data, as described above, could be obtained by using a prediction method such as Simple Regression.


This introduction to linear regression discusses a simple linear regression model with one predictor variable, and then extends it to the multiple linear regression model with at least two predictors.

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