A Step-by-step guide to master Machine Learning

The proposition of machine learning on several industries, professional field and the personnel are considered astounding by some and appalling. Ones outlook will largely depend on your profession and the work one does.

To know what the delegacy or what algorithmic rule to use to best learn from the information on a specific trouble before hand, without knowing the problem so well that you probably do not need automobile learning to Begin with.

Using the methodological analysis where, the modeller can discover the execution ceiling for the data Set before settling on a model. In many cases, a range of models will be equivalent in terminal figure of performance so the practitioner can weigh the benefits of different methodological analysis

One must investigate a suite of complex models and establish a performance ceiling, such as:

Support Vector Machines
Gradient Boosting Machines
Random Forest
Bagged Decision Trees
Neural Networks
Investigate a suite of simpler more interpretable models, such as
Generalized Linear Models
LASSO and Elastic Net Regularized Generalized Linear Models
Multivariate Adaptive Regression Splines
K Nearest Neighbors
Naive Bayes

There is a great methodological analysis to use for a one off project where you need a good answer quickly, such as within minutes or 60 minutes.

The speed of spread of accuracy on a problem across models.
Having a model that is simple to identify with and clarify to others.
A reasonably elevated worth model very quickly to adapt and apply in the algorithms too.

The other methodologies in the application of algorithms are:--

More complex methods are slower to run and return a result.
Sometimes you want the complex mode over the simpler models
The performance ceiling is pursued first, rather than last when there might be time and pressure and motivation to extract the most from the best methods

Source: HOB

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