Project assignments for Udacity's Intro to Machine Learning with Pytorch Nanodegree Program image1
- Install Anaconda and dependecies
- Clone this repository to your machine
- Create a virtual environment to install dependencies
- Activate the new virtual environment
- Install packages dependencies pip install -r requirements.txt
- Start jupyter-lab using the command jupyter notebook
Finding Donors for CharityML
Overview of Project
CharityML is a fictitious charity organization located in the heart of Silicon Valley that was established to provide financial support for people eager to learn machine learning. After nearly 32,000 letters were sent to people in the community, CharityML determined that every donation they received came from someone who was making more than $50,000 annually. To expand its potential donor base, CharityML has decided to send letters to residents of California, but to only those most likely to donate to the charity. With nearly 15 million working Californians, CharityML has brought you on board to help build an algorithm to best identify potential donors and reduce the overhead cost of sending mail. Your goal will be to evaluate and optimize several different supervised learners to determine which algorithm will provide the highest donation yield while also reducing the total number of letters being sent.
Develop code for an image classifier built with PyTorch, then convert it into a command line application.
Overview of Project
AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smartphone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications.
Train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice you'd train this classifier, then export it for use in your application.
Apply unsupervised learning techniques to identify segments of the population that form the core customer base for a mail-order sales company in Germany. These segments can then be used to direct marketing campaigns toward audiences that will have the highest expected rate of returns.
Overview of Project
Work with real-life data provided to us by our Bertelsmann partners AZ Direct and Arvato Finance Solution. The data here concerns a company that performs mail-order sales in Germany. Their main question of interest is to identify facets of the population that are most likely to be purchasers of their products for a mailout campaign. Your job as a data scientist will be to use unsupervised learning techniques to organize the general population into clusters, and then use those clusters to see which of them comprise the main user base for the company. Before applying the machine learning methods, you will also need to inspect and clean the data to convert the data into a usable form.