Python Basics, Machine Learning and Deep Learning
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Updated
May 21, 2020 - Jupyter Notebook
Python Basics, Machine Learning and Deep Learning
Numpy ( Numerical Python ) Python Package.
Computer vision program written in Python that uses OpenCV to automatically take pictures of people when they are smiling and have their eyes open. WIP.
homework for computational intelligence, predicting bitcoin price with linear and polynomial regression.
Python Project which was aimed at data wrangling, analyzing and creating visualizations using python in Jupyter notbook .
A supervised Learning model that finds out donors who earn more than $50K annually
Datascience Visualization
It's a Movie recommendation web application which is built using python programming and streamlit library using PyCharm and deployed on streamlit cloud. It's main objective is to simplify the recommendation system for any organization or a person so that he/she could get the desired movie without wasting time searching randomly.
Checking If a customer will default on the loan, if the bank provides them a loan. We will analyze the past patterns of payments by performing Exploratory Data Analysis.
Generating Abstract images using Neural Networks [ open source ]
This repository consists of numpy related work.
13 Data Analysis Examples - MovieLens 25M Dataset
13 Data Analysis Examples - Bitly Data from 1.USA.gov
Data analysis and data visualization applications with python
Deep Learning - Built an face emotion recognition model that detects the sentiment of the online classroom using live video from the webcam and real-time aggregated feedback to the instructors about the class using CNN model.
The Data Analysis Tool is a Python program that provides basic data analysis features for a given dataset.
We implemented two Centralized approaches- NMPC and Velocity Obstacle Algorithm, along with two DeCentralized approaches- Priority Safe Interval Path Planning (SIPP), and Conflict Based Search (CBS) planning.
assignments and notes for Microsoft AI Talented Program, Business Intelligence track.
Решение инженерных и экономических задач с NumPy
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