A thesis from a Phd and what he has done since graduation

Here is a thesis from a Phd

https://lib.dr.iastate.edu/etd/13537/

The title of the thesis is

A balanced approach to the multi-class imbalance problem


And after graduation, the author did not work for companies, he opens his consulting firm instead

Omni Analytics Group

https://omnianalytics.io/

one good sql tutorial and some good machine learning channels

one good sql tutorial

https://www.youtube.com/watch?v=nWeW3sCmD2k


some good machine learning channels

https://www.youtube.com/user/joshstarmer/videos


https://www.youtube.com/user/mathtutordvd/videos

https://www.youtube.com/channel/UCq8JbYayUHvKvjimPV0TCqQ/videos

https://www.youtube.com/user/edurekaIN/videos

https://www.youtube.com/channel/UC8butISFwT-Wl7EV0hUK0BQ/videos

Understanding self and __init__ method in python Class.

https://micropyramid.com/blog/understand-self-and-__init__-method-in-python-class/

self :

self represents the instance of the class. By using the "self" keyword we can access the attributesand methods of the class in python.

__init__ :

"__init__" is a reseved method in python classes. It is known as a constructor in object oriented concepts. This method called when an object is created from the class and it allow the class to initialize the attributes of a class.

classification

classification, how to do it

http://inseaddataanalytics.github.io/INSEADAnalytics/CourseSessions/Sessions67/ClassificationAnalysisReading.html

l1 and l2 regularization

l1 and l2 regularization
https://towardsdatascience.com/l1-and-l2-regularization-methods-ce25e7fc831c


https://www.linkedin.com/pulse/l1-l2-regularization-why-neededwhat-doeshow-helps-ravi-shankar

when p>> n, when we use OLS , we will have over fitting. to reduce overfitting, we use regularization, l1 and l2. l1 forces some parameters to be zero.  l2 shrinks some of the parameters to be zeros, but it tries to keep all the parameters in the models.


https://www.analyticsvidhya.com/blog/2016/01/complete-tutorial-ridge-lasso-regression-python/

Key Difference

• Ridge: It includes all (or none) of the features in the model. Thus, the major advantage of ridge regression is coefficient shrinkage and reducing model complexity.
• Lasso: Along with shrinking coefficients, lasso performs feature selection as well. (Remember the ‘selection‘ in the lasso full-form?) As we observed earlier, some of the coefficients become exactly zero, which is equivalent to the particular feature being excluded from the model.

Traditionally, techniques like stepwise regression were used to perform feature selection and make parsimonious models. But with advancements in Machine Learning, ridge and lasso regression provide very good alternatives as they give much better output, require fewer tuning parameters and can be automated to a large extend.

2. Typical Use Cases

• Ridge: It is majorly used to prevent overfitting. Since it includes all the features, it is not very useful in case of exorbitantly high #features, say in millions, as it will pose computational challenges.
• Lasso: Since it provides sparse solutions, it is generally the model of choice (or some variant of this concept) for modelling cases where the #features are in millions or more. In such a case, getting a sparse solution is of great computational advantage as the features with zero coefficients can simply be ignored.

Its not hard to see why the stepwise selection techniques become practically very cumbersome to implement in high dimensionality cases. Thus, lasso provides a significant advantage.

3. Presence of Highly Correlated Features

• Ridge: It generally works well even in presence of highly correlated features as it will include all of them in the model but the coefficients will be distributed among them depending on the correlation.
• Lasso: It arbitrarily selects any one feature among the highly correlated ones and reduced the coefficients of the rest to zero. Also, the chosen variable changes randomly with change in model parameters. This generally doesn’t work that well as compared to ridge regression.

word header and footer

wood header and footer

https://www.youtube.com/watch?v=lNdjuIYuB3o

How to Create Stunning Flowcharts in Microsoft Word

How to Create Stunning Flowcharts in Microsoft Word

https://www.youtube.com/watch?v=iiS7aAFI2Cs


https://www.youtube.com/watch?v=hjhJ3-jSBM8