Summary

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.

Expected Learning

Many of you may have already taken a course in machine learning or data science or are familiar with machine learning models.

In this course we will take a more general approach, walking through the questioning, modeling and validation steps of the model building process.

The goal is to get you to practice thinking in depth about a problem and coming up with your own solutions. Many examples we will attempt may not have one correct answer but will require you to work through the problems applying the methods we hope to illustrate throughout this class.

Syllabus

Lesson 1 - Introduction to the QMV Process

Learn about the Question, Modeling, and Validation (QMV) process of data analysis. Understand the basics behind each step and apply the QMV process to analyze on how Udacity employees choose candies!

Lesson 2 - Question Phase

We will drill in on the questioning phase of the QMV process. We’ll teach you how to turn a vague question into a statistical one that can be analyzed with statistics and machine learning. You will also analyze a Twitter dataset and try to predict when a person will tweet next!

Lesson 3 - Modeling Phase

Building upon lesson 2, you will learn how to build rigorous mathematical, statistical, and machine learning models so you can make accurate predictions. You look through the recently released U.S. medicare dataset for anomalous transactions.

Lesson 4 - Validation Phase

So how do you tell if your model is doing well? In this lesson, we will teach you some of the fundamental and important metrics that you can use to grade the performance of the models that you’ve build. You will analyze the AT&T connected cars data set and see if you can tell which driver is which by analyzing their driving patterns.

Final Project - Identify Hacking Attempts from Network Flow Logs

You will create a program that examines log data of net flow traffic, and produces a score, from 1 to 10, describing the degree to which the logs suggest a brute force attack is taking place on a server.

Required Knowledge

This is an advanced course, and the ideal students for this class are prepared individuals who have:

1. Python programming knowledge, familiarity with python tools like Ipython Notebook and data analysis libraries like Scikit-learn, Scipy, and Pandas
2. Knowledge of descriptive, inferential, and predictive statistics
3. Knowledge of calculus, especially derivatives and integrals
4. Knowledge of basic matrix algebra - matrices, vectors, determinant, identity matrix, multiplication, inverse
5. Taken Intro to Machine learning and have understanding of common supervised learning and unsupervised learning algorithms, such as SVM and k-means clustering