reddyprasade/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.

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reddyprasade/Machine-Learning-Interview-Preparation

Prepare to Technical Skills Here are the essential skills that a Machine Learning Engineer needs, as mentioned Read me files. Within each group are topics that you should be familiar with. Study Tip: Copy and paste this list into a document and save to your computer for easy referral. Computer Science Fundamentals and Programming Topics Data structures: Lists, stacks, queues, strings, hash maps, vectors, matrices, classes & objects, trees, graphs, etc. Algorithms: Recursion, searching, sorting, optimization, dynamic programming, etc. Computability and complexity: P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc. Computer architecture: Memory, cache, bandwidth, threads & processes, deadlocks, etc. Probability and Statistics Topics Basic probability: Conditional probability, Bayes rule, likelihood, independence, etc. Probabilistic models: Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc. Statistical measures: Mean, median, mode, variance, population parameters vs. sample statistics etc. Proximity and error metrics: Cosine similarity, mean-squared error, Manhattan and Euclidean distance, log-loss, etc. Distributions and random sampling: Uniform, normal, binomial, Poisson, etc. Analysis methods: ANOVA, hypothesis testing, factor analysis, etc. Data Modeling and Evaluation Topics Data preprocessing: Munging/wrangling, transforming, aggregating, etc. Pattern recognition: Correlations, clusters, trends, outliers & anomalies, etc. Dimensionality reduction: Eigenvectors, Principal Component Analysis, etc. Prediction: Classification, regression, sequence prediction, etc.; suitable error/accuracy metrics. Evaluation: Training-testing split, sequential vs. randomized cross-validation, etc. Applying Machine Learning Algorithms and Libraries Topics Models: Parametric vs. nonparametric, decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models, etc. Learning procedure: Linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods; regularization, hyperparameter tuning, etc. Tradeoffs and gotchas: Relative advantages and disadvantages, bias and variance, overfitting and underfitting, vanishing/exploding gradients, missing data, data leakage, etc. Software Engineering and System Design Topics Software interface: Library calls, REST APIs, data collection endpoints, database queries, etc. User interface: Capturing user inputs & application events, displaying results & visualization, etc. Scalability: Map-reduce, distributed processing, etc. Deployment: Cloud hosting, containers & instances, microservices, etc. Move on to the final lesson of this course to find lots of sample practice questions for each topic!

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reddyprasade/DataSet-for-ML-and-Data-Science

Freely Available Data Sets For Real world Problems

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reddyprasade/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.

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reddyprasade/Clientoclarify.github.io Fork of Clientoclarify/Clientoclarify.github.io

Company Website

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reddyprasade/Machine-Learning

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reddyprasade/Application-Vulnerability

Software security deals with securing the foundational programmatic logic of underlying software. Different from application security, software security focuses on the early stages of the software development lifecycle (SDLC) and the underlying code of an application.

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reddyprasade/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.

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reddyprasade/Deep-Learning

Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on artificial neural networks. Learning can be supervised, semi-supervised or unsupervised. Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases superior to human experts.

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reddyprasade/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).

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reddyprasade/reddyprasade

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reddyprasade/Machine-Learning-With-R

Machine Learning in R

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reddyprasade/Breast-cancer-Predication

Breast cancer symptoms vary widely — from lumps to swelling to skin changes — and many breast cancers have no obvious symptoms at all. In some cases, a lump may be too small for you to feel or to cause any unusual changes you can notice on your own. Often, an abnormal area turns up on a screening mammogram (X-ray of the breast), which leads to further testing. In other cases, however, the first sign of breast cancer is a new lump or mass in the breast that you or your doctor can feel. A lump that is painless, hard, and has uneven edges is more likely to be cancer. But sometimes cancers can be tender, soft, and rounded.

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reddyprasade/girikon_stt_LLM_model

Inspire From whisper

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reddyprasade/NER

NER

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reddyprasade/ML-Test-Assignment

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reddyprasade/Regression-on-fuel-consumption-in-canada

Datasets provide model-specific fuel consumption ratings and estimated carbon dioxide emissions for new light-duty vehicles for retail sale in Canada.

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reddyprasade/Carbon-Dioxide-Emissions-Predication-of-FuelConsumption-Data-Sets

which contains model-specific fuel consumption ratings and estimated carbon dioxide emissions for new light-duty vehicles for retail sale in Canada

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reddyprasade/Image-Segmentation

Image Segmentation with Watershed Algorithm Any grayscale image can be viewed as a topographic surface where high intensity denotes peaks and hills while low intensity denotes valleys. You start filling every isolated valleys (local minima) with different colored water (labels). As the water rises, depending on the peaks (gradients) nearby, water from different valleys, obviously with different colors will start to merge. To avoid that, you build barriers in the locations where water merges. You continue the work of filling water and building barriers until all the peaks are under water. Then the barriers you created gives you the segmentation result. This is the "philosophy" behind the watershed. You can visit the CMM webpage on watershed to understand it with the help of some animations. But this approach gives you oversegmented result due to noise or any other irregularities in the image. So OpenCV implemented a marker-based watershed algorithm where you specify which are all valley points are to be merged and which are not. It is an interactive image segmentation. What we do is to give different labels for our object we know. Label the region which we are sure of being the foreground or object with one color (or intensity), label the region which we are sure of being background or non-object with another color and finally the region which we are not sure of anything, label it with 0. That is our marker. Then apply watershed algorithm. Then our marker will be updated with the labels we gave, and the boundaries of objects will have a value of -1.

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reddyprasade/Numpy-with-Python

NumPy is the fundamental package for scientific computing with Python. It contains among other things: a powerful N-dimensional array object sophisticated (broadcasting) functions tools for integrating C/C++ and Fortran code useful linear algebra, Fourier transform, and random number capabilities

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reddyprasade/Data-Visualization-with-Matplotlib

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reddyprasade/Data-Visualization-with-Seaborn

Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.

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reddyprasade/Summerization_dataset

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reddyprasade/Telecommunications-Data-for-Predicting-Customer-Churn

We’ll use a telecommunications data for predicting customer churn. This is a historical customer data where each row represents one customer. The data is relatively easy to understand, and you may uncover insights you can use immediately. Typically it’s less expensive to keep customers than acquire new ones, so the focus of this analysis is to predict the customers who will stay with the company. This data set provides info to help you predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs. The data set includes information about: Customers who left within the last month – the column is called Churn Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies Customer account information – how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges Demographic info about customers – gender, age range, and if they have partners and dependents

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reddyprasade/Q_A_Dataset

Question and Data set For Q & A and Summarization

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reddyprasade/Computer-Vision-with-Python

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reddyprasade/Boman_app_cli

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reddyprasade/Java-JDK-1.0-src_boman_test Fork of barismeral/Java-JDK-1.0-src

java 1.0 version created Sun MicroSystems 98/07/01

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reddyprasade/interview

Test the Model

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reddyprasade/vulnrepo Fork of kac89/vulnrepo

VULNRΞPO - Free vulnerability report generator and repository end-to-end encrypted. Complete templates of issues, CWE, CVE, MITRE ATT&CK, PCI DSS, AES encryption, Nmap/Nessus/Burp/OpenVAS/Bugcrowd/Trivy issues import, Jira export, TXT/HTML/PDF report, attachments, automatic changelog, statistics, vulnerability management.

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reddyprasade/WebGoat.NET Fork of i4mmaddy/WebGoat.NET

OWASP WebGoat.NET

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reddyprasade/sentry-python Fork of getsentry/sentry-python

The official Python SDK for Sentry.io

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reddyprasade/sreeharie.github.io Fork of sreehariE/sreeharie.github.io

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reddyprasade/shiftleft-go-demo Fork of ShiftLeftSecurity/shiftleft-go-demo

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reddyprasade/shiftleft-python-demo Fork of ShiftLeftSecurity/shiftleft-python-demo

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reddyprasade/Python-Nikto-Vulnerability-Report-Tool Fork of sectool/Python-Nikto-Vulnerability-Report-Tool

Nikto Vulnerability Report Tool 🌌

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reddyprasade/ruby-goof Fork of snyk-matt/ruby-goof

Intentionally vulnerable Ruby application to show Snyk's open-source security capabilities.

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reddyprasade/numpy_Vuln_test Fork of numpy/numpy

The fundamental package for scientific computing with Python.

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reddyprasade/mahout Fork of apache/mahout

Mirror of Apache Mahout

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reddyprasade/io19 Fork of lmoroney/io19

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reddyprasade/elki Fork of elki-project/elki

ELKI Data Mining Toolkit

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reddyprasade/deeplearning4j Fork of deeplearning4j/deeplearning4j

Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learning

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reddyprasade/encog-java-core Fork of jeffheaton/encog-java-core

Encog Machine Learning Framework

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reddyprasade/dsvm Fork of dimarlosning/n1-dsvw

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reddyprasade/datasets Fork of tensorflow/datasets

TFDS is a collection of datasets ready to use with TensorFlow

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reddyprasade/cvelist Fork of CVEProject/cvelist

Pilot program for CVE submission through GitHub

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reddyprasade/Collection-of-data Fork of ShyamKumarChand/Collection-of-data

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reddyprasade/appscan-sdk Fork of AppSecDev/appscan-sdk

SDK for interacting with Application Security on Cloud

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reddyprasade/api Fork of covid19india/api

Our Database

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reddyprasade/AltoroJ_Vuln_repo Fork of HCL-TECH-SOFTWARE/AltoroJ

WARNING: This app contains security vulnerabilities. AltoroJ is a sample banking J2EE web application. It shows what happens when web applications are written with consideration of app functionality but not app security. It's a simple and uncluttered platform for demonstrating and learning more about real-life application security issues.

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reddyprasade/Hungarian-Chickenpox-Cases

A spatio-temporal dataset of weekly chickenpox cases from Hungary. The dataset consists of a county-level adjacency matrix and time series of the county-level reported cases between 2005 and 2015.

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reddyprasade/scikit-learn Fork of scikit-learn/scikit-learn

scikit-learn: machine learning in Python

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reddyprasade/pandas Fork of pandas-dev/pandas

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

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reddyprasade/conda Fork of conda/conda

A system-level, binary package and environment manager running on all major operating systems and platforms.

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reddyprasade/JavaVulnerableLab

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reddyprasade/webgoat-java Fork of i4mmaddy/webgoat-java

Test Java Valun

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reddyprasade/Data-Analysis-with-Python

In the Data Analysis with Python Certification, you'll learn the fundamentals of data analysis with Python. By the end of this certification, you'll know how to read data from sources like CSVs and SQL, and how to use libraries like Numpy, Pandas, Matplotlib, and Seaborn to process and visualize data.

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reddyprasade/railsgoat

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reddyprasade/vulnpy

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reddyprasade/Security-Dashboard

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reddyprasade/JavaCodeAudit Fork of cn-panda/JavaCodeAudit

Getting started with java code auditing

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reddyprasade/django_nv

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reddyprasade/Bias-correction-of-numerical-prediction-model-temperature-forecast-Data-Set

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reddyprasade/WebGoat

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reddyprasade/Vulnerable-Rails-App

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reddyprasade/2020-PY-101

This Python for beginners training course leads the students from the basics of writing and running Python scripts to more advanced features such as file operations, working with binary data, and using the extensive functionality of Python modules. Extra emphasis is placed on features unique to Python, such as tuples, array slices, and output formatting.

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reddyprasade/VulnerableRubyWebsite Fork of whithajess/VulnerableRubyWebsite

Vulnerable Ruby Website

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reddyprasade/railsgot Fork of i4mmaddy/railsgot

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reddyprasade/Deep-Learning-with-Tensorflow-2.x

Deep Learning with Tensorflow 2 and Keras

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reddyprasade/reddyprasade.github.io

Website Code

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reddyprasade/Global-Wheat-Detection-From-University-of-Saskatchewan

Global WHEAT Dataset is the first large-scale dataset for wheat head detection from field optical images. It included a very large range of cultivars from differents continents. Wheat is a staple crop grown all over the world and consequently interest in wheat phenotyping spans the globe. Therefore, it is important that models developed for wheat phenotyping, such as wheat head detection networks, generalize between different growing environments around the world.

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reddyprasade/Detection-of-Ovarian-Cancer

Cancer that arises from different cells of the ovaries, the paried female reproductive organ.Although it is relatively uncommon, it is often diagnosed late. The exact cause is unknown. It can be symptomless initially but lower abdominal pain is observed in advanced stages, along with other symptoms. Treatment involves surgery followed by chemotherapy, or in combination.

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reddyprasade/PatternPackage

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reddyprasade/Data-Science-With-R

The goal of “R for Data Science” is to help you learn the most important tools in R that will allow you to do data science.

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reddyprasade/Neural-Collaborative-Filtering-with-Tensorflow

we Build a Neural Collaborative Filtering with Tensor flow

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reddyprasade/Deep-Neutral-Network-Softmax-for-Recomendation-system

We will create a movie recommendation system based on the MovieLens dataset available here. The data consists of movies ratings

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reddyprasade/Natural-Language-Processing

Natural language processing (NLP) is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.

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reddyprasade/Turtle_Graphics_In_Python3

Turtle graphics is a popular way for introducing programming to kids. It was part of the original Logo programming language developed by Wally Feurzeig, Seymour Papert and Cynthia Solomon in 1967.

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reddyprasade/Machine-Learning-With-AI

Make Machine Learning

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reddyprasade/Introducation-to-julia-Program

Julia is a high-level, high-performance, dynamic programming language. While it is a general purpose language and can be used to write any application, many of its features are well-suited for high-performance numerical analysis and computational science.

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reddyprasade/A-Criterion-for-Deciding-the-Number-of-Clusters-in-a-Dataset-Based-on-Data-Depth

This paper proposes a novel idea for ¯nding the correct number of groups in a dataset based on data depth. The idea is to avoid the traditional process of running the clustering algorithm over a dataset for ffiffiffi n p times and further, ¯nding the k value for a dataset without setting any speci¯c search range for k parameter. We experiment with di®erent indices, namely CH, KL, Silhouette, Gap, CSP and the proposed method on di®erent real and synthetic datasets to estimate the correct number of groups in a dataset. The experimental results on real and synthetic datasets indicate good performance of the proposed method.

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reddyprasade/Object-Detection-with-Deployement

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reddyprasade/Machine-Learing-Model-Deployment-in-Amazon-SageMaker

Step 1. Create an Amazon SageMaker notebook instance for data preparation. In this step, you create the notebook... Step 2. Prepare the data. In this step, you use your Amazon SageMaker notebook instance to preprocess the data that you... Step 3. Train the ML model. In this step, you use your training dataset to train your machine learning model. In a new... Step 4. Deploy the model. In this step, you deploy the trained model to an endpoint, reformat and load the CSV data,... Step 6. Clean up.

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reddyprasade/Neuro-Imaging-in-Python

Nilearn is a Python module for fast and easy statistical learning on NeuroImaging data. It leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis.

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reddyprasade/COVID19-CT

## Problem Statement: Coronavirus disease 2019 (COVID-19) has infected more than 1.3 million individuals all over the world and caused more than 106,000 deaths. One major hurdle in controlling the spreading of this disease is the inefficiency and shortage of medical tests. To mitigate the inefficiency and shortage of existing tests for COVID-19, we propose this competition to encourage the development of effective Deep Learning techniques to diagnose COVID-19 based on CT images. The problem in this challenge is to classify each CT image into positive COVID-19 (the image has clinical findings of COVID-19) or negative COVID-19 ( the image does not have clinical findings of COVID-19). It’s a binary classification problem based on CT images.

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reddyprasade/statsmodels-with-Python

statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. An extensive list of result statistics are available for each estimator. The results are tested against existing statistical packages to ensure that they are correct.

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reddyprasade/PYML-2020

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reddyprasade/COVID-19-Chest-X-Ray

COVID-19 Chest X-Ray

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reddyprasade/Bird-Classifications-Problem

Birds Classification by using Scikit-learn and Scikit-image

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reddyprasade/BBC-New-Articles-Clustering-and-Classification

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reddyprasade/model-deployment-for-iris-dataset

We Deploy a Machine Learning Model in GitHub

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reddyprasade/Matplotlib

Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+. There is also a procedural "pylab" interface based on a state machine, designed to closely resemble that of MATLAB, though its use is discouraged. SciPy makes use of Matplotlib.

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reddyprasade/Pandas-Practice

Pandas

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reddyprasade/ML-With_python_pract

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reddyprasade/Machine-Learning-model-Income-Prediction-Deployment

Predict whether income exceeds $50K/yr based on census data. Also known as "Census Income" dataset

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reddyprasade/The-Street-View-House-Numbers-Machine-Learning-Model-Deployment-By-Web-Flask-API

Machine Learn Model Deployment with Flask Api

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reddyprasade/Text-Mining-with-Python

introduce the learner to text mining and text manipulation basics. The course begins with an understanding of how text is handled by python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text. The second week focuses on common manipulation needs, including regular expressions (searching for text), cleaning text, and preparing text for use by machine learning processes. The third week will apply basic natural language processing methods to text, and demonstrate how text classification is accomplished. The final week will explore more advanced methods for detecting the topics in documents and grouping them by similarity (topic modelling).

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reddyprasade/Bicycle-sharing-system-in-US

A bicycle-sharing system, public bicycle system, or bike-share scheme, is a service in which bicycles are made available for shared use to individuals on a short term basis for a price or free. Many bike share systems allow people to borrow a bike from a "dock" and return it at another dock belonging to the same system. Docks are special bike racks that lock the bike, and only release it by computer control. The user enters payment information, and the computer unlocks a bike. The user returns the bike by placing it in the dock, which locks it in place. Other systems are dockless. For many systems, smartphone mapping apps show nearby available bikes and open docks.

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reddyprasade/Deploy-Predict-Cancer-Model-In-Flask-Api

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reddyprasade/Feature-Engineering

The purpose of a feature, other than being an attribute, would be much easier to understand in the context of a problem. A feature is a characteristic that might help when solving the problem. Discover the most effective way to improve your models.

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