Prerequisites and prework

AI-generated Key Takeaways

• Google's Machine Learning Crash Course offers a flexible learning experience for users with varying levels of machine learning expertise, including beginners, those seeking a refresher, and experienced practitioners.

• The course requires prework, such as familiarity with Python, NumPy, and pandas, and has prerequisites in algebra, linear algebra, statistics, and optionally, calculus, to fully grasp the concepts.

• While focusing on core ML concepts, the course incorporates practical programming exercises using libraries like NumPy, pandas, and Keras but doesn't delve deep into specific ML APIs.

• Learners are encouraged to complete the prework, including an introductory machine learning course and tutorials for NumPy and pandas, to ensure preparedness.

• The course leverages the Colaboratory platform, offering browser-based programming exercises that require no setup and are best experienced on Chrome or Firefox desktops.

Is Machine Learning Crash Course right for you?

I have little or no machine learning background.

We recommend going through all the material in order.

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I have some background in machine learning, but I'd like a more current and complete understanding.

Machine Learning Crash Course will be a great refresher. Go through all the modules in order, or select only those modules that interest you.

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I have practical experience applying machine learning concepts to work with data and build models.

While Machine Learning Crash Course may be useful to you as a refresher of fundamental machine learning concepts, you may also want to explore some of our advanced machine learning courses, which cover tools and techniques for solving machine learning problems in a variety of domains.

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I am looking for tutorials on how to use ML APIs like Keras.

While Machine Learning Crash Course includes several programming exercises that use ML libraries such as numpy, pandas, and Keras, it is primarily focused on teaching ML concepts, and does not teach ML APIs in depth. For additional Keras resources, see the Keras Developer guides.

Please read through the following Prework and Prerequisites sections before beginning Machine Learning Crash Course, to ensure you are prepared to complete all the modules.

Prework

Before beginning Machine Learning Crash Course, do the following:

1. If you're new to machine learning, take Introduction to Machine Learning. This short self-study course introduces fundamental machine learning concepts.
2. If you are new to NumPy, do the NumPy Ultraquick Tutorial Colab exercise, which provides all the NumPy information you need for this course.
3. If you are new to pandas, do the pandas UltraQuick Tutorial Colab exercise, which provides all the pandas information you need for this course.

Programming exercises run directly in your browser (no setup required!) using the Colaboratory platform. Colaboratory is supported on most major browsers, and is most thoroughly tested on desktop versions of Chrome and Firefox.

Prerequisites

Machine Learning Crash Course does not presume or require any prior knowledge in machine learning. However, to understand the concepts presented and complete the exercises, we recommend that students meet the following prerequisites:

• You must be comfortable with variables, linear equations, graphs of functions, histograms, and statistical means.

• You should be a good programmer. Ideally, you should have some experience programming in Python because the programming exercises are in Python. However, experienced programmers without Python experience can usually complete the programming exercises anyway.

The following sections provide links to additional background material that is helpful.

Algebra

• variables, coefficients, and functions
• linear equations such as \(y = b + w_1x_1 + w_2x_2\)
• logarithms, and logarithmic equations such as \(y = ln(1+ e^z)\)
• sigmoid function

Linear algebra

• tensor and tensor rank
• matrix multiplication

Trigonometry

• tanh (discussed as an activation function; no prior knowledge needed)

Statistics

• mean, median, outliers, and standard deviation
• ability to read a histogram

Calculus (optional, for advanced topics)

• concept of a derivative (you won't have to actually calculate derivatives)
• gradient or slope
• partial derivatives (which are closely related to gradients)
• chain rule (for a full understanding of the backpropagation algorithm for training neural networks)

Python Programming

The following Python basics are covered in The Python Tutorial:

• defining and calling functions, using positional and keyword parameters

• dictionaries, lists, sets (creating, accessing, and iterating)

• for loops, for loops with multiple iterator variables (e.g., for a, b in [(1,2), (3,4)])

• if/else conditional blocks and conditional expressions

• string formatting (e.g., '%.2f' % 3.14)

• variables, assignment, basic data types (int, float, bool, str)

A few of the programming exercises use the following more advanced Python concept:

• list comprehensions

Bash Terminal and Cloud Console

To run the programming exercises on your local machine or in a cloud console, you should be comfortable working on the command line:

• Bash Reference Manual
• Bash Cheatsheet
• Learn Shell

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