Prerequisites and Prework

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Is Machine Learning Crash Course right for you?
I have little or no machine learning background.
I have some background in machine learning, but I'd like a more current and complete understanding.
I know machine learning really well, but I know little or nothing about TensorFlow.
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:

If you're new to machine learning, take Introduction to Machine Learning Problem Framing. This one-hour self-study course teaches you how to identify appropriate problems for machine learning.
If you are new to NumPy, do the NumPy Ultraquick Tutorial Colab exercise, which provides all the NumPy information you need for this course.
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. If you'd prefer to download and run the exercises offline, see these instructions for setting up a local environment.

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
logarithms, and logarithmic equations such as
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 / 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

Click Here to Apply

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