Deepesh Garg's Projects
android-kotlin-fundamentals-apps
android-kotlin-fundamentals-starter-apps
Artificial Intelligence (AI) based Crop Recommendation system is desired for providing suggestions for all the crops which may increase profitability of the farmers. The system may consider parameters of good agricultural practices. Obtain soil type, water requirement / availability, seasonal parameters (temperature ranges, humidity, etc.) along with location and advise the best crops suitable along with what is required (quality / quantity of seeds, fertilizers,etc), duration of cultivation, demand, cost of cultivation and expected revenues / profits.
An app to help guide blind people in walking.
We have made this site for our very first event, which is a Frontend Hackathon. This is available as an opensource project and if you want to contribute then either create an issue or make a PR.
This repository contains the Code Of Conduct, Rules as well as Event Slides and Material for our very first event, i.e. Front End Hackathon.
In this project, you will apply unsupervised learning techniques to identify segments of the population that form the core customer base for a mail-order sales company in Germany. These segments can then be used to direct marketing campaigns towards audiences that will have the highest expected rate of returns. The data that you will use has been provided by our partners at Bertelsmann Arvato Analytics, and represents a real-life data science task.
Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smart phone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications. In this project, you'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice you'd train this classifier, then export it for use in your application.
Google IT Automation with Python Professional Certificate - Practice files
This Repository Contains All My Practised Assignments And Labs in The Jupyter Nobooks From The Natural Language Processing Specialization Offered By Deeplearning.ai .
This is my first repo.
In this project, you will employ several supervised algorithms of your choice to accurately model individuals' income using data collected from the 1994 U.S. Census. You will then choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data. Your goal with this implementation is to construct a model that accurately predicts whether an individual makes more than $50,000. This sort of task can arise in a non-profit setting, where organizations survive on donations. Understanding an individual's income can help a non-profit better understand how large of a donation to request, or whether or not they should reach out to begin with. While it can be difficult to determine an individual's general income bracket directly from public sources, we can (as we will see) infer this value from other publically available features. The dataset for this project originates from the UCI Machine Learning Repository. The datset was donated by Ron Kohavi and Barry Becker, after being published in the article "Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid". You can find the article by Ron Kohavi online. The data we investigate here consists of small changes to the original dataset, such as removing the 'fnlwgt' feature and records with missing or ill-formatted entries.
Some Codes And Projects Of Courses Of Coursera Are Uploaded Here .
Interpretation and classification of emotions (positive, negative and neutral) within text data using text analysis techniques.
Flexiple Assignment
Central Repository of Student Code-In website
This is my First repo .
This Repository contains all my codes in Jupyter Notebooks which I practiced along with the Udacity - AWS Machine Learning Foundations Course .
This Repository Contains My Exercise Notebooks From The Udacity Computer Vision Nanodegree .
This Repository Contains Notebooks Of Projects And Mini-Projects From Udacity Nanodegree : Introduction To Machine Learning With Tensorflow
This Repository Contains Notebooks Of Projects And Mini-Projects From Udacity Nanodegree : Introduction To Machine Learning With Tensorflow
This repositary contain all my exercises and projects of Udacity Computer Vision Nanodegree Program
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent