singhrupa0212 Goto Github PK

advanced-house-price-prediction-

applied-machine-learning-explainability-techniques

Applied Machine Learning Explainability Techniques, published by Packt

assignments

credit-card-fraud-detection

Machine learning model for Credit Card fraud detection

credit-card-fraudlent

credit_card-risk-assessment

data-science-and-ml-project

My Machine Learning Projects

data-science-and-ml-projects

data-science-interview

data-science-with-python

The Data Science with Python course provides a complete overview of Data Science analytics techniques using Python. A data scientist is one of the hottest fields today and Python is a crucial skill for many Data Science roles. Expand your Data Science knowledge with this Python certification course.

data-sets-for-machine-learnig-and-data-science

Data sets

datapreprocess

dbscan-algorithm

ds-batch-2020

Data Science with Python 2020

eda

EDA

eda1

exploratory-data-analysis

feature-selection-techniques

food-classification

A CNN model is trained to classify 3 classes of food in the food-101 data set.

krish-naik

lending-club-cs

Upgrad assignment

linearregressionblog

machine-learning-problems-datasets

We currently maintain 488 data sets as a service to the machine learning community. You may view all data sets through our searchable interface. For a general overview of the Repository, please visit our About page. For information about citing data sets in publications, please read our citation policy. If you wish to donate a data set, please consult our donation policy. For any other questions, feel free to contact the Repository librarians.

machine-learning-with-scikit-learn-python-3.x

In general, a learning problem considers a set of n samples of data and then tries to predict properties of unknown data. If each sample is more than a single number and, for instance, a multi-dimensional entry (aka multivariate data), it is said to have several attributes or features. Learning problems fall into a few categories: supervised learning, in which the data comes with additional attributes that we want to predict (Click here to go to the scikit-learn supervised learning page).This problem can be either: classification: samples belong to two or more classes and we want to learn from already labeled data how to predict the class of unlabeled data. An example of a classification problem would be handwritten digit recognition, in which the aim is to assign each input vector to one of a finite number of discrete categories. Another way to think of classification is as a discrete (as opposed to continuous) form of supervised learning where one has a limited number of categories and for each of the n samples provided, one is to try to label them with the correct category or class. regression: if the desired output consists of one or more continuous variables, then the task is called regression. An example of a regression problem would be the prediction of the length of a salmon as a function of its age and weight. unsupervised learning, in which the training data consists of a set of input vectors x without any corresponding target values. The goal in such problems may be to discover groups of similar examples within the data, where it is called clustering, or to determine the distribution of data within the input space, known as density estimation, or to project the data from a high-dimensional space down to two or three dimensions for the purpose of visualization (Click here to go to the Scikit-Learn unsupervised learning page).

ml-coursera-python-assignments

Python assignments for the machine learning class by andrew ng on coursera with complete submission for grading capability and re-written instructions.

mnist-keras

Using various CNN techniques on the MNIST dataset

new-data-sets

projects_2021

Projects 2021

python-basic-for-all-3.x

We are going to Learn Python, it is a powerful multi-purpose programming language created by Guido van Rossum. It has simple easy-to-use syntax, making it the perfect language for someone trying to learn computer programming for the first time. This is a comprehensive guide on how to get started in Python, why you should learn it and how you can learn it. However, if you knowledge of other programming languages and want to quickly get started with Python.

python-pattern-programming