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admn5016_2022

bayesian-regression-with-infinitely-broad-prior-gaussian-parameter-distribution-

Class implements the Bayesian regression with Gaussian prior Parameter distribution this code only considers the Infinitely Broad Prior and the Equivalent kernel.

best-readme-template

An awesome README template to jumpstart your projects!

cognitiveclass.ai-data-analysis-with-python

These are the notebooks from cognitive class's Data Analysis with Python. Take the free course at :https://cognitiveclass.ai/courses/data-analysis-python/

cognitiveclass.ai-python-for-data-science

Course link: https://cognitiveclass.ai/courses/python-for-data-science/ Python for Data Science these are notebooks from the course This introduction to Python will kickstart your learning of Python for data science, as well as programming in general. This beginner-friendly Python course will take you from zero to programming in Python in a matter of hours. Upon its completion, you'll be able to write your own Python scripts and perform basic hands-on data analysis using our Jupyter-based lab environment. If you want to learn Python from scratch, this free course is for you.

datasets

A collection of datasets of ML problem solving

deep_learning_pipelines_part-1

meetup labs

deep_learning_pipelines_part_2

dynamic-time-alignment-k-means-kernel-clustering-for-time-sequence-clustering

This is a matlab implementation of Dynamic Time-Alignment (DTA) K-Means Kernel Clustering For Time Sequence Clustering. The code is similar to what I used in my paper [1]. The code first calculates the DTA Kernel matrix, then performs clustering on time series of different lengths.

freelancer-theme

Jekyll theme based on Freelancer Start Bootstrap theme

imputation-of-missing-values-matlab-

datasets contain missing values, often encoded NaNs or other placeholders. Instead of discarding rows containing missing values that comes a price of losing data which may be valuable. One can impute the missing values, i.e., to infer them from the known part of the data. The Imputer function provides basic strategies for imputing missing values, either using the mean, the median or the most frequent value of the column in which the missing values are located, Just like the Scikit learn version.

kernel-nearest-neighbor-algorithm-in-python-

This code extended to the well-known nearest-neighbor algorithm for classification so that kernels can be used

majority-vote-function-for-matlab-

ml4seti

Machine Learning for SETI

multinomial-2d-toy-data-for-classification-

This function generates some multinomial toy-data for classification Input a:column vector repenting decision boundary in the form: f(x)=a[0]+a[1]x1+a[2]x2+a[3]x1x2+a[4]x1^2+a[5]x2^3 NumberSamples: number of samples Output Out[1]:y lables such that f(xi)<=0 yi=1 and f(xi)>0 yi=2 Out[0]:X 2d feature vectors with rows corresponding to x Needs:numpy

playing_games_with_ai_mc

scala-101

Draft version for Scala 101

suxkl-next-instagram-pinterest

next-instagram-pinterest- my project for IBM

test_hosting

testing hosting on githubb

this_is_a_test

this_is_my_second_test

xzceb-flask_eng_fr

flask_enf_fr