https://youtu.be/ZzuRy_y-xW0
Sep 01
lec 2 performance measurement categories for performance measurements of tracking applications such
https://youtu.be/p88UOH_QPXk
Aug 29
Misc. Models in Machine Learning
Factor Analysis:
GPCM/GGPCM:
GPCM Gaussian Process Convolution Model
Generalised Gaussian Process Convolution Model (GGPCM), which is a generalisation of the Gaussian Process Convolution Model presented by Tobar et al.
What is GPCM Model? Generic Predictive Computational Model
Example Research on GPCM: https://ieeexplore.ieee.org/document/9944777
https://en.wikipedia.org/wiki/Mixture_model
https://www.sciencedirect.com/topics/medicine-and-dentistry/mixture-model
Aug 26
Normal/Gaussian Distribution: Understanding data for Machine Learning and Data Science Projects.
Normal/Gaussian Distribution: Bell Curve
https://mathworld.wolfram.com/NormalDistribution.html
Univariate Normal Distribution:
BiVariate Normal/Gaussian Distribution
https://www.probabilitycourse.com/chapter5/5_3_2_bivariate_normal_dist.php
Multi Variate Random variable
https://en.wikipedia.org/wiki/Multivariate_normal_distribution
Aug 25
Misc. Plots for Data Science Projects
Titanic Dataset: Double Decker Plot
Berkeley Admission Data:
https://www.thoughtco.com/uc-berkeley-admissions-787148
Geyser Data: With Contours
Aug 25
What are Association rule and APriori Algorithm. How to calculate the related measures.
Apriori[1] is an algorithm for frequent item set mining and association rule learning over relational databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database. The frequent item sets determined by Apriori can be used to determine association rules which highlight general trends in the database: this has applications in domains such as market basket analysis.
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I know not that clear
https://www.ibm.com/docs/sl/sdm/18.0.0?topic=settings-association-rule-scoring-options
Calculation:
Ref: https://rasbt.github.io/mlxtend/user_guide/frequent_patterns/association_rules/
https://www.kdnuggets.com/2016/04/association-rules-apriori-algorithm-tutorial.html
Support
Confidence:
Lift
Aug 24
How to use the Swagger Editor to design your API (or to create an API specification)
Use the Insert menu to create/generate misc API specification Items:
Also, watch some training videos such as:
Example use of the menu shown above to design your API:
You can use the above approaches or the approach below:
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To keep things simple, you can just go there: https://editor.swagger.io/ (make sure to use expand/collapse to see what I am saying below)
You will see an editor with an API specification for a Pet store. You can just reuse this API specification and add your specification on top of that.
One simple approach can be: think about every single object (data) that you want the users to be able to access through your API, you create a path for each such object. For each such object, you can create paths/subpaths as the following image is showing. Here Pet, store, and user are the objects. there are multiple paths for an object such as user, user/login, user/logout or /pet, /pet/{petID}.
Then you see under the path /pet : some REST verbs
The for example under post (if you expand post), you see: (some stuff, you need to know/understand, otherwise you can just follow their style)
You can do such for all object paths (for your application), additional paths for objects, and for inside object REST verbs.
