After completing the challenge course Udacity and Bertelsmann offered me a scolarship for taking this nanodegree program.
Te goal of the program is to help us learn machine learning techniques, algorithms and models while creating a job-ready portfolio of completed projects
Apply supervised learning techniques on data collected for the US census to help CharityML (a fictitious charity organization) identify groups of people that are most likely to donate to their cause.
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 that was making more than $50,000 annually.
To expand their 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 overhead cost of sending mail.
Your goal will be 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.
Define and train a neural network in TensorFlow that learns to classify images; going from image data exploration to network training and evaluation
Implementing an image classification application using a deep neural network. This application will train a deep learning model on a dataset of images. It will then use the trained model to classify new images.
You will develop your code in a Jupyter notebook to ensure your implementation works well. Key skills demonstrated include TensorFlow and neural networks, and model validation and evaluation.
Study a real dataset of customers for a company, and apply several unsupervised learning techniques in order to segment customers into similar groups and extract information that may be used for marketing or product improvement.
In this project, you will apply unsupervised learning techniques on product spending data collected for customers of a wholesale distributor in Lisbon, Portugal to identify customer segments hidden in the data.
You will first explore and pre-process the data by scaling each product category and then identifying (and removing) unwanted outliers. With the cleaned customer spending data, you will apply PCA transformations to the data and implement clustering algorithms to segment the transformed customer data.
Finally, you will compare the segmentation found with an additional labeling and consider ways this information could assist the wholesale distributor with future service changes. Key skills demonstrated include data cleaning, dimensionality reduction with PCA, and unsupervised clustering