Prerequisites and prework  |  Machine Learning  |  Google for Developers (2024)

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Please read through the following Prework andPrerequisites sections before beginning Machine LearningCrash 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 setuprequired!) using the Colaboratoryplatform. Colaboratory is supported on most major browsers, and is mostthoroughly tested on desktop versions of Chrome and Firefox.

Prerequisites

Machine Learning Crash Course does not presume or require any prior knowledge inmachine learning. However, to understand the concepts presentedand complete the exercises, we recommend that students meet thefollowing 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 someexperience programming in Python becausethe programming exercises are in Python. However, experiencedprogrammers without Python experience can usually complete the programmingexercises anyway.

The following sections provide links to additional background materialthat 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 anactivation 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)
• gradientor slope
• partial derivatives(which are closely related to gradients)
• chain rule(for a full understanding of the backpropagation algorithmfor 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 andconditional 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 advancedPython 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