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Machine Learning Engineer Nanodegree
Instructor: Arpan Chakraborty, David Joyner, Luis Serrano, Sebastian Thrun, Vincent Vanhoucke, Katie Malone
Platform: Udacity
Price: Free
Prerequisites: statistics, calculus, linear algebra

Intermediate Python programming knowledge Intermediate statistical knowledge ntermediate calculus and linear algebra mastery

Machine learning represents a key evolution in the fields of computer science, data analysis, software engineering, and artificial intelligence. This program will teach you how to become a machine learning engineer, and apply predictive models to massive data sets in fields like finance, healthcare, education, and more. Syllabus: 1. P0: Titanic Survival Exploration: In this optional project, you will create decision functions that attempt to predict survival outcomes from the 1912 Titanic disaster based on each passenger’s features, such as sex and age. You will start with a simple algorithm and increase its complexity until you are able to accurately predict the outcomes for at least 80% of the passengers in the provided data. This project will introduce you to some of the concepts of machine learning as you start the Nanodegree program. 2. P1: Predicting Boston Housing Prices: The Boston housing market is highly competitive, and you want to be the best real estate agent in the area. To compete with your peers, you decide to leverage a few basic machine learning concepts to assist you and a client with finding the best selling price for their home. Luckily, you’ve come across the Boston Housing dataset which contains aggregated data on various features for houses in Greater Boston communities, including the median value of homes for each of those areas. Your task is to build an optimal model based on a statistical analysis with the tools available. This model will then used to estimate the best selling price for your client’s home. 3. P2: Finding Donors for CharityML: 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 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.