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sentry-python

The official Python SDK for Sentry.io

shiftleft-go-demo

shiftleft-python-demo

simple-linear-regression

Simple Linear Regression used to Predicate

sockets-with-python-

Socket Programming with Python

sreeharie.github.io

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.

support-vector_machine-for-cat-dog-image-classification

Support Vector Machine For Image Classification

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

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).

the-street-view-house-numbers-machine-learning-model-deployment-by-web-flask-api

Machine Learn Model Deployment with Flask Api

theano-practices

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features: tight integration with NumPy – Use numpy.ndarray in Theano-compiled functions. transparent use of a GPU – Perform data-intensive computations much faster than on a CPU. efficient symbolic differentiation – Theano does your derivatives for functions with one or many inputs. speed and stability optimizations – Get the right answer for log(1+x) even when x is really tiny. dynamic C code generation – Evaluate expressions faster. extensive unit-testing and self-verification – Detect and diagnose many types of errors. Theano has been powering large-scale computationally intensive scientific investigations since 2007. But it is also approachable enough to be used in the classroom (University of Montreal’s deep learning/machine learning classes).

time_series_analysis

Time Series Analysis with Python numpy pandas

top-zomato-restaurants-in-bengaluru

The basic idea of analysing the Zomato dataset is to get a fair idea about the factors affecting the aggregate rating of each restaurant, establishment of different types of restaurant at different places, Bengaluru being one such city has more than 12,000 restaurants with restaurants serving dishes from all over the world. With each day new restaurants opening the industry hasn’t been saturated yet and the demand is increasing day by day. In spite of increasing demand it however has become difficult for new restaurants to compete with established restaurants. Most of them serving the same food. Bengaluru being an IT capital of India. Most of the people here are dependent mainly on the restaurant food as they don't have time to cook for themselves. With such an overwhelming demand of restaurants it has therefore become important to study the demography of a location. Hence build a model to predict the rating of the each restaurants.

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.

uber-data-analysis-project

Data is the oil for uber. With data analysis tools and great insights, Uber improve its decisions, marketing strategy, promotional offers and predictive analytics. With more than 15 million rides per day across 600 cities in 65 countries, Uber is growing rapidly with Data Science starting from data visualization and gaining insights that help them to craft better decisions. Data Science tools play a key role in every operation of Uber.

unstructured-images-to-structure-array-in-python

Machine learning, Python uses image data in the form of a NumPy array, i.e., [Height, Width, Channel] format. To enhance the performance of the predictive model, we must know how to load and manipulate images. In Python, we can perform one task in different ways. We have options from Numpy to Pytorch and CUDA, depending on the complexity of the problem. By the end of this tutorial, you will have hands-on experience with: Loading and displaying an image using Matplotlib, OpenCV and Keras API Converting the loaded images to the NumPy array and back Conducting basic manipulation of an image using the Pillow and NumPy libraries and saving it to your local system. Reading images as arrays in Keras API and OpenCV

vulnerable-rails-app

vulnerablerubywebsite

Vulnerable Ruby Website

vulnpy

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.

webgoat

webgoat-java

Test Java Valun

webgoat.net

OWASP WebGoat.NET

zero-to-hero-with-python-2.x

Learn Python For any one and any Where but i need you time to learn