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100-days-of-java

Java

100-days-of-python-repeat

100DaysOfCode is literally about doing some coding every day. That's why this course has practical hands-on exercises for every single one of the 100 days. These exercises range in length from 10 minutes to about 1-hour a day. Here is the 3-day project for the error handling chapter

100daysofcode

The Github Profile will commit my 100 days challenge of coding in Python and I Will upload the Question with the solution also to learn Python

admindashboard

ai-cheatsheets-for-basics

A cheat sheet (also cheatsheet) or crib sheet is a concise set of notes used for quick reference. Cheat sheets are so named because they may be used by students without the instructor's knowledge to cheat on a test.

anz-virtual-internship

Data@ANZ is about mining and linking datasets to develop stories that matter and challenge the status quo, to deliver on ANZ’s purpose “to shape a world where people and communities thrive”

apple-stock-prediction-using-lstm

Stock Prices Prediction is a very interesting area of Machine Learning. Personally, I always have interest in the applications of this field. Machine Learning became very useful to the Stock Market Forecasting over the last years, and today, many investment companies are using Machine Learning to make decisions in the Stock Market.

arpit1920

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

c-codechef-problems

C++ is a general-purpose programming language created by Bjarne Stroustrup as an extension of the C programming language, or "C with Classes".CodeChef is a competitive programming community of programmers from across the globe. CodeChef was started as an educational initiative in the year 2009 by Directi, an Indian software company.

courses

Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1

covid-19-xray-detection

Detecting COVID-19 in X-ray images with Keras, TensorFlow, and Deep Learning

credit-card-fraud-detection

The increase in the usage of the credit card by the people, the transactions done by credit card increases dramatically in the world. With this drastic increase in the usage of credit cards, the number of fraudulent also increases enormously & it is very difficult to identify the difference between a fraudulent transaction and normal transaction. American Express-issued credit card to 53.7 Million users, however, recorded Rs. 73380 fraud in a year on average. Credit card fraudulent causes serious losses to the individual and the organization. The credit card issuing companies offer credit card fraud detection applications to the users and individuals for their safety. This paper focuses on the different algorithms used for credit card fraud detection and find the optimal algorithm for classification of credit card fraud detection. It uses Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbors, Support Vector Machine, eXtreme Gradient Boosting, Random Forest and computes the accuracy, AUC-ROC values for all the classifiers.

data-science--cheat-sheet

Cheat Sheets

data-science-best-resources

Carefully curated resource links for data science in one place

data-science-interviews

Data science interview questions and answers

data-science-roadmap

Repository to store sample python programs for python learning

data-structure-and-algorithms-sheets

This repository consists of all the DSA Sheets solutions which are on Internet by named Youtubers

deep-dive-into-deep-learning

Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised

deep-learning

deep-learning-all-coursera-solutions

Its a formal help to all of you where I have uploaded all the solutions of assignments. You should use just a glimpse to understand that and nothing else. It may affect your skills as well.

deep-learning-book

Deep learning is an AI function that mimics the workings of the human brain in processing data for use in detecting objects, recognizing speech, translating languages, and making decisions. Deep learning AI is able to learn without human supervision, drawing from data that is both unstructured and unlabeled.

deloitte-virtual-internship-pptx

The Solutions of Deloitte Virtual Internships of Cloud Technology

django-api-developement

driver-drowsie-system

Driver drowsiness detection is a car safety technology which helps prevent accidents caused by the driver getting drowsy. Various studies have suggested that around 20% of all road accidents are fatigue-related, up to 50% on certain roads

extractive-text-summerization

Summarization systems often have additional evidence they can utilize in order to specify the most important topics of document(s). For example, when summarizing blogs, there are discussions or comments coming after the blog post that are good sources of information to determine which parts of the blog are critical and interesting. In scientific paper summarization, there is a considerable amount of information such as cited papers and conference information which can be leveraged to identify important sentences in the original paper. How text summarization works In general there are two types of summarization, abstractive and extractive summarization. Abstractive Summarization: Abstractive methods select words based on semantic understanding, even those words did not appear in the source documents. It aims at producing important material in a new way. They interpret and examine the text using advanced natural language techniques in order to generate a new shorter text that conveys the most critical information from the original text. It can be correlated to the way human reads a text article or blog post and then summarizes in their own word. Input document → understand context → semantics → create own summary. 2. Extractive Summarization: Extractive methods attempt to summarize articles by selecting a subset of words that retain the most important points. This approach weights the important part of sentences and uses the same to form the summary. Different algorithm and techniques are used to define weights for the sentences and further rank them based on importance and similarity among each other. Input document → sentences similarity → weight sentences → select sentences with higher rank. The limited study is available for abstractive summarization as it requires a deeper understanding of the text as compared to the extractive approach. Purely extractive summaries often times give better results compared to automatic abstractive summaries. This is because of the fact that abstractive summarization methods cope with problems such as semantic representation, inference and natural language generation which is relatively harder than data-driven approaches such as sentence extraction. There are many techniques available to generate extractive summarization. To keep it simple, I will be using an unsupervised learning approach to find the sentences similarity and rank them. One benefit of this will be, you don’t need to train and build a model prior start using it for your project. It’s good to understand Cosine similarity to make the best use of code you are going to see. Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them. Since we will be representing our sentences as the bunch of vectors, we can use it to find the similarity among sentences. Its measures cosine of the angle between vectors. Angle will be 0 if sentences are similar. All good till now..? Hope so :) Next, Below is our code flow to generate summarize text:- Input article → split into sentences → remove stop words → build a similarity matrix → generate rank based on matrix → pick top N sentences for summary.

flipkart_automation

BrowserStack Automation Assignment

free-spotify

Making Spotify Ads free with an amount of $0

java-micro-services

Implementation of Microservices using springboot on port 9001

learndatascience

Open Content for self-directed learning in data science