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NumPy Masterclass: Python on Data Science & Machine Learning

Name: NumPy Masterclass: Python on Data Science & Machine Learning
Rating: 4.6 (370 reviews)

The Complete Hands-On Guide to NumPy - Used for Linear Algebra, Scientific Computing, Machine Learning and Data Science

Highest Rated

Created byTM Quest

Last updated 10/2025

English

What you'll learn

Learn to confidently work with vectors and matrices in NumPy.
Learn basic functionality like sorting, calculating means, and finding max/min values.
Learn to draw line plots, bar plots, and scatterplots.
Learn to generate different types of random vectors.
Learn to modify and reshape matrices to your advantage.
Learn Boolean indexing and advanced slicing to extract useful information.
Learn to do basic linear algebra in NumPy like solving linear systems, calculating inverses, and more!
Get an understanding of how ndarrays work and utilize this to create fast code.
Learn Fourier transforms with NumPy and use this to manipulate images and audio.
Learn advanced linear algebra like the QR decomposition and partial least squares.
Learn how to preserve your NumPy objects in different formats.
Learn about neighboring libraries and that NumPy is used everywhere in Python's data science stack.

Coding Exercises

This course includes our updated coding exercises so you can practice your skills as you learn.

Course content

12 sections • 74 lectures • 5h 30m total length

Introduction to the Course4:05
In this lecture, we are introducing NumPy and its advantages.
Download all the Material0:05
Here you can download all the material for the course in a Zip file.
Installing Anaconda1:21
In this lecture, we are installing Anaconda which we are going to use throughout the course.
Markdown Cells3:53
In this lecture, we are learning about markdown cells in Jupyter notebooks.
Code Cells4:14
In this lecture, we are learning about code cells in Jupyter notebooks.
Importing NumPy5:23
In this lecture, we are going to learn how to import the NumPy package.

Introduction1:21
In this video, we give you an outline of what we will cover in this module.
Creating Vectors6:01
In this video, we will learn how to create and index vectors.
Create a vector
Changing Values
Basic Operations5:41
In this video, we will show you the basic operations between vectors in NumPy.
Basic Operations
NumPy Datatypes5:52
In this video, we discuss the various data types that NumPy have.
Slicing Vectors5:17
In this video, we will show you how slicing works with NumPy vectors.
Slicing
Sorting Vectors3:41
In this video, we will show you how sorting works in NumPy.
Copies vs. Views4:28
In this video, we will explain the difference between copies and views in NumPy.
Vectors
Aggregate Funcitons5:01
In this video, we will show you how to use aggerate functions (like sum and mean) to calculate interesting summaries of NumPy vectors.
Exercise Set - Temperature Data0:59
In this exercise set, we will be working with temperature data from New York!

Introduction0:46
Introduction to universal functions and plotting.
Universal Functions2:53
In this lecture, we are going to learn to use universal functions in NumPy.
Universal Functions
Function Plot7:13
In this lecture, we are going to learn to use NumPy together with MatPlotLib to plot functions.
Universal and Plotting
Bar and Scatter Plot6:56
In this lecture, we are going to learn to use NumPy together with MatPlotLib to plot bar and scatter plot.
Exercise Set - Temperature Data Continued1:58
In this exercise set, we will continue working with temperature data from New York.

Introduction1:31
In this video, we will introduce the topics that we will go through in the module.
Generators and Random Integers5:29
In this video, we show you how to generate random integers in NumPy.
Dice Values
Random, Shuffle, and Choice5:04
In this video, we show you how to use the functions random, shuffle, and choice.
Random Number Generator
The Normal Distribution5:35
In this video, we will show you how to work with the normal distribution in NumPy.
Basic Statistics5:46
In this video, we will explain how to calculate basic statistics in NumPy.
Mean, Median and Standard Deviation
Finding Unique Values3:26
In this video, we will explain how to find the unique elements in an array.
Exercise Set - Linear Regression1:16
In this exercise set, we will be going through linear regression and practising the concepts in this module.

Introduction1:01
This is the introduction video to this module.
Making and Modifying Matrices5:44
In this lecture, we are going to introduce matrices/2d-arrays.
Getting Rows and Columns
Attributes of a Matrix6:42
In this lecture, we are going to learn about the attributes of a matrix.
Changing the Shape of a Matrix6:50
In this lecture, we are going to learn how to change the shape of a matrix.
Changing the Shape
Specifying an Axis5:44
In this lecture, we are going to learn how to calculate the mean and sum with respect to columns or rows.
Taking the Maximum Over Rows
Boolean Matrices7:15
In this lecture, we are going to learn how to work with Boolean matrices.
Exercise Set - Rain Data2:08
This video goes through the exercise set of this module.

Introduction1:13
In this introduction, we give an outline of what we will cover in this module.
Basic Broadcasting5:00
In this video, we will give some basic examples of broadcasting.
Broadcasting Rules7:12
In this video, we discuss in detail the broadcasting rules of NumPy.
Broadcasting
2D Slicing4:50
In this video, we show you how slicing works for 2D arrays (matrices).
Slicing Practice
Advanced Indexing3:53
In this video, we will explain some advanced indexing features that NumPy has.
Finding Even and Odd Numbers
Exercise Set - Monochromatic Images1:34
In this exercise set, we will be working with monochromatic images (images with a single color channel).

Introduction0:50
This video is the introduction video to the linear algebra module.
Basic Linear Algebra4:57
In this lecture, we are going to explore some basic linear algebra operations.
Cross Product and Length7:28
In this lecture, we are going to explore the cross-product and norm in NumPy.
Cross and Dot Product
Matrix Operations7:24
In this lecture, we are going to explore the matrix product and transpose in NumPy.
Matrix Operations
Solving Linear Systems I5:50
This lecture is about solving linear systems in NumPy.
Linear Systems
Solving Linear Systems II4:47
This lecture is a continuation of solving linear systems in NumPy.
Exercise Set - Basic Linear Algebra2:15
This video is an introduction to the exercise set in this section.

Introduction1:22
In this video, we will give an outline of what we will cover in the module.
Making Higher Dimensional Arrays5:47
In this video, we will show you how to make general ndarrrays.
Higher Dimensional Arrays
Slicing and Aggregate Functions4:51
In this video, we will show you how to do slicing and aggregate functions on higher-dimensional arrays.
Using Aggregate Functions
Colored Images7:16
In this video, we will work with images as an example of 3D arrays.
What are Strides?5:27
In this video, we will explain how strides work and why this is useful to know.
Exercise Set - Color Images1:32
In this exercise set, we will be working with RGB images.

Introduction1:21
This is the introduction video to the Fourier transform.
Complex Numbers4:41
In this lecture, we are going to explore complex vectors.
Fourier Transforms I5:54
In this lecture, we are going to explore the 1-dimensional Fourier transform.
Fourier Transforms II6:56
In this lecture, we are going to continue exploring the 1-dimensional Fourier transform.
Smoothing a Signal6:00
In this lecture, we are going to smooth a signal using the Fourier transform in NumPy.
2D Fourier Transforms8:04
This lecture is all about the 2D Fourier transform.
Exercise Set - Fourier Transforms3:24
In this exercise, we are going to explore an audio signal using NumPy.

Introduction1:49
In this video, we will give an outline of the topics covered in this module.
Finding Eigenvalues and Eigenvectors7:11
In this video, we will explain how to find eigenvectors and eigenvalues in NumPy.
Types of Matrices9:13
In this video, we will explain three types of matrices; diagonal matrices, orthogonal matrices, and upper-triangular matrices.
Working with Orthogonal Matrices
QR Decomposition7:27
In this video, we will explain the QR decomposition.
QR Decomposition and Eigenvalues
Partial Least Squares5:56
In this video, we will explain the method of partial least squares.
Exercise Set - Quadratic Approximations & More1:24
In this exercise set, we will be practising our advanced linear algebra skills.

Requirements

A basic understanding of variables, lists, and functions in Python.
Some knowledge of mathematics (linear algebra, complex numbers) is useful.
No previous experience with NumPy is required!
A willingness to write loads of NumPy code!

Description

Do you want to master NumPy and unlock your potential in data science? This course is your comprehensive, hands-on introduction to the foundational library of modern Python computing!

NumPy is the absolute core building block for essential data science and machine learning libraries like Pandas, Scikit-learn, and PyTorch. By mastering it, you gain the technical edge needed for advanced topics like linear algebra, image processing, and fast numerical computations. If you want to start a career in Data Science or understand the engine behind Machine Learning in Python, this course is for you.

What You'll Master in this Hands-On Python Course:

This course will teach you everything you need to professionally use NumPy for scientific computing. We start with the basics and rapidly move into advanced techniques crucial for complex data science tasks.

Foundation: Introduction to NumPy arrays, N-dimensional arrays, and the fundamental concepts of vectors and matrices.
Data Analysis Tools: Leverage Universal Functions (ufuncs), Randomness, and Statistics to analyze and explore data efficiently in Python.
Linear Algebra for ML: Master Basic and Advanced Linear Algebra operations, which are the backbone of all Machine Learning algorithms.
Advanced Techniques: Understand Broadcasting and Advanced Indexing to write fast, memory-efficient Python code.
Real-World Scientific Computing: Apply NumPy to specialized fields like Fourier Transforms, Image Processing, and data manipulation for Simple Machine Learning models.
Data Management: Learn professional methods for Saving and Loading Data efficiently.

Why Choose Our Course? Expertise Meets Practical Application

We are Eirik and Stine, a couple passionate about creating high-quality, impactful courses. Eirik has taught both Python and NumPy at the university level, while Stine has developed curriculum used in university courses utilizing NumPy for data science.

We don't shy away from the technical depth that will make you a standout practitioner. The course is filled with:

In-Video Exercises to reinforce concepts immediately.
Large, Project-Style Assignments (in Jupyter Notebooks) on awesome topics like Audio Processing, Linear Regression (a core Machine Learning task), and Image Manipulation.

By the end of our course, you will be highly proficient with NumPy and have a rock-solid technical foundation for pursuing Data Science and Machine Learning roles in Python.

No Risk, Just Learning

You're covered by Udemy’s 30-day money-back guarantee. Preview some free lessons and see why our teaching style is perfect for you. Start your journey into Scientific Computing and Data Science today!

Who this course is for:

Anyone who wants to get a good understanding of NumPy.
Students who want to implement topics like linear algebra, machine learning, and image processing in Python.
Python developers who are curious about NumPy and data science!

NumPy Masterclass: Python on Data Science & Machine Learning

What you'll learn

Explore related topics

Coding Exercises

Course content

Introduction6 lectures • 19min

Working with Vectors9 lectures • 38min

Universal Functions and Plotting5 lectures • 20min

Randomness and Statistics7 lectures • 28min

Making and Modifying Matrices7 lectures • 35min

Broadcasting and Advanced Indexing6 lectures • 24min

Basic Linear Algebra7 lectures • 34min

Understanding ndarrays6 lectures • 26min

Fourier Transforms7 lectures • 36min

Advanced Linear Algebra6 lectures • 33min

Requirements

Description

Who this course is for: