Lamyaa-Zayed/Image-Compression

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Lamyaa-Zayed/Image-Compression

Lamyaa-Zayed

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Image Compression project using K-means and SVD algorithms.

Air_Defence_System

Air Defence System contains: (Three micro-controllers Atmega16) Radar, Wind_Speed meter, Missile_Launcher as inputs and Target_Serine, Alarm_Serine as outputs. Radar gives you the number of detected target, Wind_Speed meter gives you analog signal which is propotional to the measured wind speed, Missile_Launcher has 8 missiles; reads a digital value to determine which missile out of 8 should be launched. when radar detects target, sys immediately activates the Target_Serine. If the reading of radar indicates more than 8 targets; system activates Alarm_Serine.

Plotly-Dashboard-Visualization-Project

Creating Dashboard visualization project for Students' Performance Levels.

Jupyter Notebook

Rating-Competitors-Using-Infer.NET

I show you how to get started with the Infer.NET library by computing ratings for a set of competitors using head-to-head results. A good way to get an idea of probabilistic programming. This demo sets up six competitors (think sports teams): Angels, Bruins, Comets, Demons, Eagles and Flyers.

Investigating-Netflix-Movies-and-Guest-Stars-in-The-Office

In this project, you’ll apply the skills you learned in Introduction to Python and Intermediate Python to solve a real-world data science problem. You’ll press “watch next episode” to discover if Netflix’s movies are getting shorter over time and which guest stars appear in the most popular episode of "The Office", using everything from lists and loops to pandas and matplotlib. You’ll also gain experience in an essential data science skill — exploratory data analysis. This will allow you to perform critical tasks such as manipulating raw data and drawing conclusions from plots you create of the data. Press play to begin!

Jupyter Notebook

Pyramids-Data-Exploration-Using-Java

Deal with Egyptian Pyramids CSV dataset. https://www.kaggle.com/lsind18/egyptianpyramids Prepare it and print the basic data about each pyramid. Dataset preparation/transformation, Build a Pyramid class to define a Dataset preparation/transformation, Build a PyramidCSVDAO class to: Read pyramids.csv file. Create List of Pyramids objects for each pyramid in the csv file Do not forget to validate the values you make all needed conversion. Prototype for all information you will extract from CSV for each pyramid.

Numerical-Optimization-for-Machine-Learning

implementation for different optimizations protocols: Batch Gradient Descent, Mini-Batch Gradient Descent, Stochastic Gradient Descent, Momentum Gradient Descent, NAG Algorithm, Adagrad Algorithm, RMS-Prop Algorithm, Adam Algorithm.

Jupyter Notebook

MVVM-WPF-CS-Demo

Creating UI by WPF App designed by MVVM architecture with two user controls, when clicking on customer name or its id then immediately his attached order information appears.

Deep-Learning

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WPF-CS-Drag-and-Drop_Rx

Creating a Drag and Drop demo by WPF C# program on Visual Studio 2019, making shapes which drags and couldn't drop in any position except one big rectangle.

PySpark-For-BigData_Streaming-and-GraphFrames.

Implementing Streaming process by net cat server and Graph Frames operations based on Ubuntu Py-Spark.

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ArabicDialectsClassification

Create Flask App to detect and multi-class classify the Arabic dialects.

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PySpark-for-BigData_ML_Classification.

Implementing Machine Learning Model for Classification Data set By Py-Spark For Big Data.

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Kaggle-Competition-Dry-Beans-Classification

Evaluation ITI - AI Pro Timeline It's simple. You are given a set of features extracted from the shape of the beans in images and it's required to predict the type of each bean. There are 7 bean types in this dataset.

Jupyter Notebook

Simple_Calculator

A simple_Calculator with KEYPAD and LCD interfaced by ATmega32 microcontroller

LED_Matrix

8x8 LED Matrix that display Time with multiplexing time

Kaggle-competition-Arabic-Hand-Letters-Predictions

You're given images of handwritten Arabic characters and you're required to recognize the character.

Jupyter Notebook

Reinforcement-Learning-with-Tensorflow-Keras

Reinforcement Learning implementation with Tensor flow Keras

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Computer-Vision-Labs

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HyperparameterTuning-Script-using-Kerastuner-and-Tensorboard

Hyperparameter Tuning Script using Keras_tuner and Tensorboard

Airbnb_KaggleCompetition

Target is to predict which country a new user's first booking destination will be. Dataset Link on Kaggle: https://www.kaggle.com/c/airbnb-recruiting-new-user-bookings/data

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UnSupervised-Machine-Learning-Problems.

Implementing different models for different data sets with Unsupervised ML.

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NLP

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X-Chart-Titanic-data-exploration-with-Java-Maven-Application.

Add the Xchart dependency and build an application to load the Titanic data set and extract the data to be displayed on charts as described in the previous slides

Air_Defence_System_with_RTOS

System designed by real-time operating system with Dynamic and Static Architectures and making a scheduler between tasks.

Algorithms

Workshop Algorithms

Jupyter Notebook

I_Phone-system-with-RTOS

System designed by real-time operating system with Dynamic and Static Architectures and making a scheduler between tasks.

RX-with-WPF

making timer with RX.

I_Phone_Basic

Designing the digital control system which controls Screen_Luminance, Speaker and Ringer, Assume the phone has two modes CALL_Mode & NO_CALL_Mode and both has its own setting, then phone waits for the ACCEPT_CALL signal or END_CALL signal.

PySpark-for-BigData_ML_Regression

Implementing Machine Learning Model for Regression Data set By Py-Spark For Big Data.

Jupyter Notebook

Smile-Project-to-explore-Training-and-Testing-data-for-Machine-Learning

Smile-Project-to-explore-Training-and-Testing-data-for-Machine-Learning

Kaggle-Competition_Seoul-Bike-Rental-Prediction

You are provided hourly rental data along with weather data. For this competition, the training set is comprised of the first 20 days of each month, while the test set is the 21th to the end of the month. You must predict the total count of bikes rented during each hour covered by the test set, using only information available prior to the rental period.

Jupyter Notebook

Car-Crashes-Severity-Prediction

# Car Crashes' Severity Prediction ## Get Started: You are given information about the environment of the car crash and you're required to predict the severity of the crash out of 4 level. The predictive system is already built but it needs some data of good quality. You need to prepare the dataset and train the prediction systems. This predictive system will be helpful to anticipate the resources to engage by San Francisco Municipality depending on its severity. ### Link The link to this challenge can be found here: [Kaggle Link](https://www.kaggle.com/c/car-crashes-severity-prediction)

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Recommender-systems-Algorithms

Recommender Systems Project contains content based, user based, item based, collaborative filtering and many similarity matrices.

Jupyter Notebook

Bayesian-Inference-and-Modeling-using-Factor-Graphs

Develop Bayesian inference and modeling using Factor Graphs for Probabilistic machine learning user interface to facilitate building new graph models for front end developers designed with WPF by C# and Infra.net languages using Microsoft Visual Studio.

MVVM-CS-WPF-UI-Proj

Creating UI by WPF designed by MVVM to add first and last names of customers then hit submit buttons to appears his full name and its added date but it should insert both names else the submit button will get deactivated.

Better-Strings-with-Java-Apache-netbeans

Your goal is to make a method called betterString that takes two Strings and a lambda that says whether the first of the two is “better”. The method should return that better String; i.e., if the function given by the lambda returns true, the betterString method should return the first String, otherwise betterString should return the second String. Given a String, the task is to check whether a string contains only alphabets or not. use isLetter() method of Character class.

Titanic-Data-Preparations-with-Java-Maven-Application.

load the Titanic data set provided as a csv file • Using joinery and Tablesaw perform some data exploration activities such as getting information about the data (min, max, standard deviation, etc.) • Create 2 DataFrame Objects and try to join, merge, add columns of specific datatypes to both DataFrames (perform these activities using both libraries)

Java-Final-Project-ITI_AI

Task: • Build all java needed classes (POJO , DAO, web service and a tester client for the web service) • Make a web service to get the following from the data set: 1. Read data set and convert it to dataframe or Spark RDD and display some from it. 2. Display structure and summary of the data. 3. Clean the data (null, duplications) 4. Count the jobs for each company and display that in order (What are the most demanding companies for jobs?) 5. Show step 4 in a pie chart 6. Find out What are it the most popular job titles? 7. Show step 6 in bar chart 8. Find out the most popular areas? 9. Show step 8 in bar chart 10. Print skills one by one and how many each repeated and order the output to find out the most important skills required? 11. Factorize the YearsExp feature and convert it to numbers in new col. (Bounce ) 12. Apply K-means for job title and companies (Bounce )

City-and-Country-Data-Manipulation-with-Java

Develop and application that reads two files for cities and countries and store each in a List. Each city entry contains code, name, continent, Surface Area, population Create a map that uses the country code as keys and a list of cities as the value for each country. For a given country code sort the cities according to the population