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aaic-notes

These are my personal notes, feel free to use them for your reference.

artificial-intelligence-deep-learning-machine-learning-tutorials

A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.

awesome-data-engineering

A curated list of data engineering tools for software developers

best-data-science-resources

best-of-ml-python

🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.

book_recommendation_system

These project is part of the “Machine Learning & Advanced Machine Learning” curriculum as capstone projects at Almabetter

company_classification

Company_Classification_Clustering Problem Statement : We are given with web scraped data of various businesses and companies. We need to somehow categorize these businesses and companies across a standard taxonomy (consists of term names and labels that are specific to an organization's information and unique to how that business operates). So that, business can leverage this information and target potential companies. Overview of DATASET Website: The website of the company/business Company Name: The company/business name Homepage Text : Visible homepage text H1: The heading 1 tags from the html of the home page H2: The heading 2 tags from the html of the home page H3: The heading 3 tags from the html of the home page Navlink text: The visible titles of navigation links on the homepage (Ex: Home, Services, Product, About Us, Contact Us) Meta keywords: The meta keywords in the header of the page html for SEO Meta description: The meta description in the header of the page html for SEO

company_classification_clustering

Problem Statement : Classify businesses and companies across a standard taxonomy. This dataset comes with pre-classified companies along with data from the website. The main objective is to cluster companies based on their description on the website.

dual-region-rishabh

excel-to-terraform-convertor

This script will convert excel spreadsheets in the format of the examples included in this repository.

fk-dual-region-test

Dual region test

get-to-know

google-cloud-4-words

The Google Cloud Developer's Cheat Sheet

google-cloud-python

Google Cloud Client Library for Python

ipl-t20-cricket-analysis

Analyzing the data of IPL-T20 from season 2008-2019

linear-regression_implementation

Here, we will be implementing Linear Regression on Car price prediction with a Business problem statement.

metamask-extension

:globe_with_meridians: :electric_plug: The MetaMask browser extension enables browsing Ethereum blockchain enabled websites

ml-course-notes

🎓 Sharing course notes on all topics related to machine learning, NLP, and AI.

mobile-price-range-prediction

The objective is to find out some relation between features of a mobile phone(eg:- RAM,Internal Memory, etc) and its selling price. In this problem, we do not have to predict the actual price but a price range indicating how high the price is.

mobile_pricerange_prediction

Problem Statement In the competitive mobile phone market, companies want to understand sales data of mobile phones and factors which drive the prices. The objective is to find out some relation between features of a mobile phone(eg:- RAM, Internal Memory, etc) and its selling price. In this problem, we do not have to predict the actual price but a price range indicating how high the price is.

rishabh1928

My Github Profile

spam-detector-sample-github

Developing a Python-based spam detector using the Naive Bayes approach.

ted-talks-views-prediction

Objective is to build a predictive model, which could help in predicting the views of the videos uploaded on the TEDx website.

tensorflow-course

:satellite: Simple and ready-to-use tutorials for TensorFlow

xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow