Ameer Azam (AMEERAZAM08)

AMEERAZAM08

User data from Github https://github.com/AMEERAZAM08

Company:Pixis AI

Location:bangalore

GitHub:@AMEERAZAM08

Twitter:@Ameerazam18

Ameer Azam's repositories

google-maps-services-python

Python client library for Google Maps API Web Services

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sample.starter_notebooks

Notebooks showing Streams applications written in Python

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Audio-Classification-Resources

Best Collection of Articles and code for Audio Classification

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Data-Structures-In-C

(Incomplete, In Continuation) Implementation of Data Structures like Stacks, Queues, etc. using C programming language

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CodeFights.py

My Python3 code from CodeFight challenges.

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project_chat_application

This is a code repository for the corresponding YouTube video. In this tutorial we are going to build and deploy a real time chat application. Covered topics: React.js, Node.js, Express.js, and Socket.io.

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bangalore-november-2018-batch-SomdyutiBhat

bangalore-november-2018-batch-SomdyutiBhat created by GitHub Classroom

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IdenProf

IdenProf dataset is a collection of images of identifiable professionals. It is been collected to enable the development of AI systems that can serve by identifying people and the nature of their job by simply looking at an image, just like humans can do.

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AutoRCCar

OpenCV Python Neural Network Autonomous RC Car

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get-started-python

A Python application and tutorial that use Flask framework to provide a REST API to receive requests from the UI. The API then persists the data to a Cloudant database.

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ATPBetting

A strategy for tennis matches betting

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final-yr-projectqA

Accident-Prediction

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Data_Science_Interview_Guide

These are the tips for "5 Steps to Pass Data Science Interviews" By Siraj Raval on Youtube

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modern_portfolio

Responsive portfolio website

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Real_time_Object_detection_TF

This is an implementation of tensor flow object detection API for running it in Real time through Webcam

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Titanic-Machine-Learning-from-Disaster

Start here if... You're new to data science and machine learning, or looking for a simple intro to the Kaggle prediction competitions. Competition Description The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships. One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class. In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy. Practice Skills Binary classification Python and R basics

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MusicGenerator

Experiment diverse Deep learning models for music generation with TensorFlow

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