g10draw

Auto-Flask-App-Skeleton-Creator

This simple script creates the basic structure of a flask application with virtually activated command prompt, opens sublime text editor from app root directory and also pre-installs few modules.

bostan_housing

In this project, you will apply basic machine learning concepts on data collected for housing prices in the Boston, Massachusetts area to predict the selling price of a new home. You will first explore the data to obtain important features and descriptive statistics about the dataset. Next, you will properly split the data into testing and training subsets, and determine a suitable performance metric for this problem. You will then analyze performance graphs for a learning algorithm with varying parameters and training set sizes. This will enable you to pick the optimal model that best generalizes for unseen data. Finally, you will test this optimal model on a new sample and compare the predicted selling price to your statistics.

chatbot

data_preprocessing_tutorial

A mini tutorial of data preprocessing using scikit-learn package.

design-resources-for-developers

Curated list of design and UI resources from stock photos, web templates, CSS frameworks, UI libraries, tools and much more

django_poll_app

A Django demo appliation

image_classification

In this project, you'll classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. The dataset will need to be preprocessed, then train a convolutional neural network on all the samples. You'll normalize the images, one-hot encode the labels, build a convolutional layer, max pool layer, and fully connected layer. At then end, you'll see their predictions on the sample images.

movie_recommendation_engine

This is a simple movie recommendations web model, which uses collaborative filtering to recommend movies.

movie_review_classifier

This ml model classifies the movie reviews entered by the audience into positive or negative opinions.

smartcab

In this project you will apply reinforcement learning techniques for a self-driving agent in a simplified world to aid it in effectively reaching its destinations in the allotted time. You will first investigate the environment the agent operates in by constructing a very basic driving implementation. Once your agent is successful at operating within the environment, you will then identify each possible state the agent can be in when considering such things as traffic lights and oncoming traffic at each intersection. With states identified, you will then implement a Q-Learning algorithm for the self-driving agent to guide the agent towards its destination within the allotted time. Finally, you will improve upon the Q-Learning algorithm to find the best configuration of learning and exploration factors to ensure the self-driving agent is reaching its destinations with consistently positive results.

g10draw/image_classification

In this project, you'll classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. The dataset will need to be preprocessed, then train a convolutional neural network on all the samples. You'll normalize the images, one-hot encode the labels, build a convolutional layer, max pool layer, and fully connected layer. At then end, you'll see their predictions on the sample images.

g10draw/Auto-Flask-App-Skeleton-Creator

This simple script creates the basic structure of a flask application with virtually activated command prompt, opens sublime text editor from app root directory and also pre-installs few modules.

g10draw/movie_review_classifier

This ml model classifies the movie reviews entered by the audience into positive or negative opinions.

g10draw/movie_recommendation_engine

This is a simple movie recommendations web model, which uses collaborative filtering to recommend movies.

g10draw/smartcab

In this project you will apply reinforcement learning techniques for a self-driving agent in a simplified world to aid it in effectively reaching its destinations in the allotted time. You will first investigate the environment the agent operates in by constructing a very basic driving implementation. Once your agent is successful at operating within the environment, you will then identify each possible state the agent can be in when considering such things as traffic lights and oncoming traffic at each intersection. With states identified, you will then implement a Q-Learning algorithm for the self-driving agent to guide the agent towards its destination within the allotted time. Finally, you will improve upon the Q-Learning algorithm to find the best configuration of learning and exploration factors to ensure the self-driving agent is reaching its destinations with consistently positive results.

g10draw/bostan_housing

In this project, you will apply basic machine learning concepts on data collected for housing prices in the Boston, Massachusetts area to predict the selling price of a new home. You will first explore the data to obtain important features and descriptive statistics about the dataset. Next, you will properly split the data into testing and training subsets, and determine a suitable performance metric for this problem. You will then analyze performance graphs for a learning algorithm with varying parameters and training set sizes. This will enable you to pick the optimal model that best generalizes for unseen data. Finally, you will test this optimal model on a new sample and compare the predicted selling price to your statistics.

g10draw/chatbot

g10draw/data_preprocessing_tutorial

A mini tutorial of data preprocessing using scikit-learn package.

g10draw/design-resources-for-developers

Curated list of design and UI resources from stock photos, web templates, CSS frameworks, UI libraries, tools and much more

g10draw/django_poll_app

A Django demo appliation

g10draw/ecommerce_app

A E-Commerce application developed using Django Framework.

g10draw/finding_donors

In this project, you will apply supervised learning techniques and an analytical mind on data collected for the U.S. census to help CharityML (a fictitious charity organization) identify people most likely to donate to their cause. You will first explore the data to learn how the census data is recorded. Next, you will apply a series of transformations and preprocessing techniques to manipulate the data into a workable format. You will then evaluate several supervised learners of your choice on the data, and consider which is best suited for the solution. Afterwards, you will optimize the model you've selected and present it as your solution to CharityML. Finally, you will explore the chosen model and its predictions under the hood, to see just how well it's performing when considering the data it's given.

g10draw/flask-weather-app

g10draw/FlaskTube

An Flask application that filters youtube according to your preferences.

g10draw/fullstack-nanodegree-vm

g10draw/machine-learning

Content for Udacity's Machine Learning curriculum

g10draw/Machine-learning-without-any-libraries

This is a collection of some of the important machine learning algorithms which are implemented with out using any libraries. Libraries such as numpy and pandas are used to improve computational complexity of algorithms

g10draw/mail_with_attachment

A simple python script to send an email with attachment.

g10draw/photobook

An application to post and share photos with your friends

g10draw/python-cheatsheet

Comprehensive Python Cheatsheet

g10draw/self_analysis

Flask application for logging financial and food data.

g10draw/social-media-app

g10draw/titanic_project

In 1912, the ship RMS Titanic struck an iceberg on its maiden voyage and sank, resulting in the deaths of most of its passengers and crew. In this introductory project, we will explore a subset of the RMS Titanic passenger manifest to determine which features best predict whether someone survived or did not survive.