Model Building and Validation

This course will teach you how to start from scratch in answering questions about the real world using data. Machine learning happens to be a small part of this process. The model building process involves setting up ways of collecting data, understanding and paying attention to what is important in the data to answer the questions you are asking, finding a statistical, mathematical or a simulation model to gain understanding and make predictions.

All of these things are equally important and model building is a crucial skill to acquire in every field of science. The process stays true to the scientific method, making what you learn through your models useful for gaining an understanding of whatever you are investigating as well as make predictions that hold true to test.

We will take you on a journey through building various models. This process involves asking questions, gathering and manipulating data, building models, and ultimately testing and evaluating them.

• Introduction to the QMV Process
• Learn about the Question, Modeling, and Validation (QMV) process of data analysis.,Understand the basics behind each step.,Apply the QMV process to analyze on how Udacity employees choose candies!
• Question Phase
• Learn how to turn a vague question into a statistical one that can be analyzed with statistics and machine learning.,Analyze a Twitter dataset and try to predict when a person will tweet next!
• Modeling Phase
• Build rigorous mathematical, statistical, and machine learning models to make accurate predictions.,Look through the recently released U.S. medicare dataset for anomalous transactions.
• Validation Phase
• Learn fundamental metrics to grade the performance of your models.,Analyze the AT&T connected cars data set.,See if you can tell the drivers apart by analyzing their driving patterns.
• Identify Hacking Attempts from Network Flow Logs
• Create a program that examines log data and scores the likelihood that a brute force attack is taking place on a server.