About This Course
Machine Learning is a first-class ticket to the most exciting careers in data analysis today. As data sources proliferate along with the computing power to process them, going straight to the data is one of the most straightforward ways to quickly gain insights and make predictions. Machine learning brings together computer science and statistics to harness that predictive power. It’s a must-have skill for all aspiring data analysts and data scientists, or anyone else who wants to wrestle all that raw data into refined trends and predictions. This is a class that will teach you the end-to-end process of investigating data through a machine learning lens. It will teach you how to extract and identify useful features that best represent your data, a few of the most important machine learning algorithms, and how to evaluate the performance of your machine learning algorithms.
Why Take This?
In this course, you’ll learn by doing! We’ll bring machine learning to life by showing you fascinating use cases and tackling interesting real-world problems like self-driving cars. For your final project you’ll mine the email inboxes and financial data of Enron to identify persons of interest in one of the greatest corporate fraud cases in American history. When you finish this introductory course, you’ll be able to analyze data using machine learning techniques, and you’ll also be prepared to take our Data Analyst Nanodegree. We’ll get you started on your machine learning journey by teaching you how to use helpful tools, such as pre-written algorithms and libraries, to answer interesting questions.
Prerequisites and Requirements
To succeed in this course, you must be proficient at programming in Python, basic statistics, and data science. If you need a refresher on any of these topics, you can check out these courses: - [Intro to Computer Science](https://www.udacity.com/course/cs101) (You should know basic data structures and control statements, and be able to write and import functions.) - [Inferential Statistics](https://www.udacity.com/course/ud201) - [Descriptive Statistics](https://www.udacity.com/course/ud827) One additional course that would be nice to have is Intro to Data Science, as this will get you familiar with scientific problem-solving. However, completion of that class isn't required for success. We will also use a tiny bit of git, which you can also learn about on Udacity. One thing that we don’t require is previous exposure to machine learning. If you’re a machine learning beginner, you’re in the right place.
This class will teach you the end-to-end process of investigating data through a machine learning lens, and you'll apply what you've learned to a real-world data set.