Introduction to NumPy
Using Python for efficient numerical computing.
46
Getting Started with Anaconda Notebooks
Course Overview and Learning Objectives
Why NumPy and Vectorization?
Declaring an Array
Declaring Higher Dimensional Arrays
Exercise: Declare an Array
Anaconda Assistant: Overview
Anaconda Assistant - Thomas
Indexing Arrays
Basic Math Operators
Math Functions
Aggregating Functions
Iterating Arrays
Matrix Multiplication
Exercise: Mean Calculation
Loading Data Files into NumPy
Loading Data from pandas
Loading Data from SQL
Loading an Image
Exercise:NumpPy ndarray
Slicing Arrays
Reshaping Arrays
Copying vs. Viewing Arrays
Exercise:Reshape Pixel Data
Concatenating Arrays
Stacking Arrays
Splitting Arrays
Exercise:Split and Concatenate
Searching and Filtering Arrays
Sorting Arrays
Exercise: Element Extraction
Why Use Random Data?
Generating Random Numbers
Generating Data from Probability Distributions-Normal Distribution
Generating Data from Probability Distributions-Binomial Distribution
Generating Data from Probability Distributions-Exponential Distribution
Randomly Selecting Data
Monty Hall Problem with Monte Carlo
Exercise: Write Code for a Video Game
Linear Regression with Hill Climbing
Linear Regression with scikit-learn
Customer Queue Simulation
Neural Network with scikit-learn
Exercise: Perform a Linear Regression
Summary and Further Reading Resources
End of Course Survey
Learner Testimonials
An excellent building block!
Ryan Nichols
I was surprised by the thorough curriculum and additional topics covered. A truly great resource for anyone who wants to build a solid foundation.
I was surprised by the thorough curriculum and additional topics covered. A truly great resource for anyone who wants to build a solid foundation.
Read Less