ahmedmuoawad Goto Github PK

badgewell-data-cleaning

data-science

:bar_chart: Path to a free self-taught education in Data Science!

data-transformation-task

deep-learning-coursera

Deep Learning Specialization by Andrew Ng, deeplearning.ai.

deep-learning-keras-tensorflow

Introduction to Deep Neural Networks with Keras and Tensorflow

deep-learning-specialization-coursera

Deep Learning Specialization by Andrew Ng on Coursera.

deeplearning.ai-summary

This repository contains my personal notes and summaries on DeepLearning.ai course. I've enjoyed every little bit of the course hope you enjoy my notes too.

deeplearning_coursera

Deep Learning Specialization by Andrew Ng, deeplearning.ai.

detectron

FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

emojify

Turn your facial expression into an emoji

graphicalpainter

A desktop Java based application to illustrate and visualize the drawing algorithms taken in this course. It Can draw multiple shapes with variety of algorithms .

graphproject

A simple GUI to illustrate how the algorithms work in a computer graphic.

ipython-notebooks

A collection of IPython notebooks covering various topics.

movie-classfiction-pased-on-it-s-arabic-subtitle

classfiy English movies by using it's Arabic subtitle

movie-classfiction-pased-on-it-s-arabic-subtitle-1

classfiy English movies by using it's Arabic subtitle

nlp-with-python-for-machine-learning-essential-training

With the increased amount of data publicly available and the increased focus on unstructured text data, understanding how to clean, process, and analyze that text data is tremendously valuable. If you have some experience with Python and an interest in natural language processing (NLP), this course can provide you with the knowledge you need to tackle complex problems using machine learning. Instructor Derek Jedamski provides a quick summary of basic natural language processing (NLP) concepts, covers advanced data cleaning and vectorization techniques, and then takes a deep dive into building machine learning classifiers. During this last step, Derek shows how to build two different types of machine learning models, as well as how to evaluate and test variations of those models. Topics include: What are NLP and NLTK? Using regular expressions - Using stemming and lemmatizing, Methods to vectorize raw data Building and evaluating machine learning classifiers

python-machine-learning-book-2nd-edition

The "Python Machine Learning (2nd edition)" book code repository and info resource

sframe

SFrame: Scalable tabular and graph data-structures built for out-of-core data analysis and machine learning.

sign-language

A very simple CNN project.