Such models can either be Resampling is the method that consists of drawing repeated samples from the original data samples.
It is a non-parametric method of statistical inference.
We did a lot of exercises on Bayesian Analysis, Markov Chain Monte Carlo, Hierarchical Modeling, Supervised and Unsupervised Learning.
This experience deepens my interest in the Data Mining academic field and convinces me to specialize further in it.
Recently, I completed the Statistical Learning online course on Stanford Lagunita, which covers all the material in the Intro to Statistical Learning book I read in my Independent Study.
Now being exposed to the content twice, I want to share the 10 statistical techniques from the book that I believe any data scientists should learn to be more effective in handling big datasets.Before moving on with these 10 techniques, I want to differentiate between statistical learning and machine learning.I wrote one of the most popular Medium posts on machine learning before, so I am confident I have the expertise to justify these differences: is done by making sure that the sum of all the distances between the shape and the actual observations at each point is as small as possible.In order to understand the concept of resampling, you should understand the terms Usually for linear models, ordinary least squares is the major criteria to be considered to fit them into the data.The next 3 methods are the alternative approaches that can provide better prediction accuracy and model interpretability for fitting linear models.Additionally, this is an exciting research area, having important applications in science, industry, and finance.Ultimately, statistical learning is a fundamental ingredient in the training of a modern data scientist.As Josh Wills put it, I personally know too many software engineers looking to transition into data scientist and blindly utilizing machine learning frameworks such as Tensor Flow or Apache Spark to their data without a thorough understanding of statistical theories behind them.So comes the study of statistical learning, a theoretical framework for machine learning drawing from the fields of statistics and functional analysis. It is important to understand the ideas behind the various techniques, in order to know how and when to use them.It uses experimental methods, rather than analytical methods, to generate the unique sampling distribution.It yields unbiased estimates as it is based on the unbiased samples of all the possible results of the data studied by the researcher.