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Dimensionality Reduction: Machine Learning in Python

Name: Dimensionality Reduction: Machine Learning in Python
Rating: 4.3 (17 reviews)

Learn Dimensionality Reduction in Python. Become a Data Scientist expert! Everything you need to get the job you want!

Created byLucas Bazilio

Last updated 3/2025

English

What you'll learn

Master Visualization and Dimensionality Reduction in Python
Become an advanced, confident, and modern data scientist from scratch
Become job-ready by understanding how Dimensionality Reduction really works behind the scenes
Apply robust Machine Learning techniques for Dimensionality Reduction
Master Machine Learning Tools such as PCA, LLE, TSNE, Multidimensional Scaling, ISOMAP, Fisher Discriminant Analysis, etc.
How to think and work like a data scientist: problem-solving, researching, workflows
Get fast and friendly support in the Q&A area
Practice your skills with 10+ challenges and assignments (solutions included)

Course content

10 sections • 36 lectures • 9h 51m total length

Google Colab for Programming in Python3:08
In this lecture we explain how to use google colab for programming in Python.

Introduction to PCA4:44
On this lesson we introduce the Principal Component Analysis and give a brief background to the technique.
Introduction to the Dataset23:05
On this lesson we introduce the dataset crabs.csv.
Later we will use PCA for the visualization of this dataset.
Initial Visualization30:16
On this lesson we perform a basic exploration of the data set and an initial visualization.
At the end we discuss the importance of applying dimensionality reduction techniques.
On the following lesson we will apply PCA.
Using PCA1:05:12
On this lesson we apply the Principal Component Analysis technique to reduce the dimensionality of our dataset. We visualize the 2 Principal Components and also 3 Principal Components studying the separation of the classes.
Explanation of PCA17:17
On this lesson we explain how the Principal Component Analysis technique works.

Introduction to LLE2:59
On this lesson we introduce the Localy Linear Embedding (LLE) algorithm.
Locally Linear Embedding Algorithm3:39
On this lesson we introduce the steps that are followed in the LLE algorithm.
On future lessons we will apply this method in practise using Python.
Introduction to the Dataset14:38
On this lesson we introduce the dataset crabs.csv.
Later we will use LLE for the dimensionality reduction and visualization of this dataset.
Using LLE27:39
On this lesson we use the Locally Linear Embedding technique to reduce the dimensionality of our dataset.
We also visualize the new dimensional space with 2 components. This is a visualization in 2 dimensions.
LLE with 3 Dimensions23:38
On this lesson we use the Locally Linear Embedding technique to reduce the dimensionality of our dataset.
Now we use 3 components. At the end we do a visualization of the new dimensional space with 3 dimensions. With this, we end the practise of Locally Linear Embedding.

Introduction to t-SNE5:04
On this lesson we make a brief introduction to the t-Stochastic Neighbor Embedding dimensionality reduction technique.
Dataset0:45
On this lesson we mention that we will use the crabs.csv dataset on this section.
This is a dataset we already worked with previously. Those that are familiar with this
dataset can skip the following lesson and just go to the next one.
Introduction to the Dataset14:38
On this lesson we introduce the dataset crabs.csv.
Later we will use t-SNE for the visualization of this dataset.
t-SNE on Raw Data35:09
On this lesson we apply the t-Stochastic Neighbor Embedding technique on the original dataset of the crabs. At the end, we visualize the separation between the classes using 2 dimensions and 3 dimensions.
t-SNE on Scaled Data16:28
On this lesson we apply the t-Stochastic Neighbor Embedding technique on the scaled dataset of the crabs. At the end, we visualize the separation between the classes using 2 dimensions and 3 dimensions.
t-SNE on Standardized Data16:56
On this lesson we apply the t-Stochastic Neighbor Embedding technique on the standardized dataset of the crabs. At the end, we visualize the separation between the classes using 2 dimensions and 3 dimensions.

Introduction to MDS3:18
On this lesson we introduce the Multidimensional Scaling dimensionality reduction technique.
Using MDS with 2 Dimensions20:40
On this lesson we apply the MDS dimensionality reduction technique in the crabs dataset.
At the end, we visualize the separation between classes in 2 Dimensions.
Using MDS with 3 Dimensions19:08
On this lesson we apply the MDS dimensionality reduction technique in the crabs dataset.
At the end, we visualize the separation between classes in 3 Dimensions.

Introducción to ISOMAP2:55
On this lesson we make a brief introduction to the ISOMAP dimensionality reduction technique.
ISOMAP with 2 Dimensions28:59
On this lesson we use the ISOMAP technique to reduce the dimensionality of our dataset.
We also visualize the new dimensional space with 2 components. This is a visualization in 2 dimensions.
ISOMAP with 3 Dimensions27:45
On this lesson we use the ISOMAP technique to reduce the dimensionality of our dataset.
We also visualize the new dimensional space with 3 components. This is a visualization in 3 dimensions.

Introduction to Fisher Discriminant Analysis2:43
On this lesson we make a brief introduction to the Fisher Discriminant Analysis dimensionality reduction technique.
Dataset Information0:47
On this lesson we mention that we will use the crabs.csv dataset on this section.
This is a dataset we already worked with previously. Those that are familiar with this
dataset can skip the following lesson and just go to the next one.
Introduction to the Dataset14:38
On this lesson we introduce the dataset crabs.csv.
Later we will use Fisher Discriminant Analysis for the visualization of this dataset and the separation of the crabs.
Fisher Discriminant Analysis with 2 Dimensions27:08
On this lesson we use the Fisher Discriminant Analysis technique to reduce the dimensionality of our dataset.
We also visualize the new dimensional space with 2 components. This is a visualization in 2 dimensions.
Fisher Discriminant Analysis with 3 Dimensions23:25
On this lesson we use the Fisher Discriminant Analysis technique to reduce the dimensionality of our dataset.
We also visualize the new dimensional space with 3 components. This is a visualization in 3 dimensions.

Images0:31
On this lesson we explain that images can be transformed for use by dimensionality reduction methods by converting them to arrays of numeric values.
Introduction to Image Dataset17:28
On this lesson we introduce the image dataset digits.
Later we will use Dimensionality Reduction techniques for the visualization of this dataset and the separation of the images.
Locally Linear Embedding24:14
On this lesson we use the Locally Linear Embedding technique to reduce the dimensionality of our dataset.
We visualize a new dimensional space with 2 components in 2 dimensions.
We then also visualize a new dimensional space with 3 components in 3 dimensions.
Principal Component Analysis21:38
On this lesson we use Principal Component Analysis to reduce the dimensionality of our dataset.
We visualize a new dimensional space with 2 components in 2 dimensions.
We then also visualize a new dimensional space with 3 components in 3 dimensions.
Fisher Discriminant Analysis25:24
On this lesson we use the Fisher Discriminant Analysis technique to reduce the dimensionality of our dataset.
We visualize a new dimensional space with 2 components in 2 dimensions.
We then also visualize a new dimensional space with 3 components in 3 dimensions.
ISOMAP20:32
On this lesson we use the ISOMAP technique to reduce the dimensionality of our dataset.
We visualize a new dimensional space with 2 components in 2 dimensions.
We then also visualize a new dimensional space with 3 components in 3 dimensions.

Requirements

No data science experience is necessary to take this course.
Any computer and OS will work — Windows, macOS or Linux. We will set up your code environment in the course.

Description

You’ve just stumbled upon the most complete, in-depth Dimensionality Reduction course online.

Whether you want to:

- build the skills you need to get your first Data Scientist job

- move to a more senior software developer position

- become a computer scientist mastering in data science and machine learning

- or just learn dimensionality reduction to be able to work on your own data science projects quickly.

...this complete Dimensionality Reduction Masterclass is the course you need to do all of this, and more.

This course is designed to give you the Dimensionality Reduction skills you need to become an expert data scientist. By the end of the course, you will understand Visualization/Dimensionality Reduction extremely well and be able to use the techniques on your own projects and be productive as a computer scientist and data analyst.

What makes this course a bestseller?

Like you, thousands of others were frustrated and fed up with fragmented Youtube tutorials or incomplete or outdated courses which assume you already know a bunch of stuff, as well as thick, college-like textbooks able to send even the most caffeine-fuelled coder to sleep.

Like you, they were tired of low-quality lessons, poorly explained topics, and confusing info presented in the wrong way. That’s why so many find success in this complete Visualization/Dimensionality Reduction course. It’s designed with simplicity and seamless progression in mind through its content.

This course assumes no previous data science experience and takes you from absolute beginner core concepts. You will learn the core Visualization/Dimensionality Reduction techniques and master data science. It's a one-stop shop to learn Visualization/Dimensionality Reduction. If you want to go beyond the core content you can do so at any time.

Here’s just some of what you’ll learn

(It’s okay if you don’t understand all this yet, you will in the course)

All the essential Dimensionality Reduction techniques: PCA, LLE, t-SNE, ISOMAP... Their arguments and expressions needed to fully understand exactly what you’re coding and why - making programming easy to grasp and less frustrating.
You will learn the answers to questions like What is a High Dimensionality Dataset, What are rules and models and to reduce the dimensionality and Visualize complex decisions

Complete chapters on Dimensionality of Datasets and many aspects of the Dimensionality Reduction mechanism (the protocols and tools for building applications) so you can code for all platforms and derestrict your program’s user base.

How to apply powerful machine learning techniques using Dimensionality Reduction.

What if I have questions?

As if this course wasn’t complete enough, I offer full support, answering any questions you have.

This means you’ll never find yourself stuck on one lesson for days on end. With my hand-holding guidance, you’ll progress smoothly through this course without any major roadblocks.

There’s no risk either!

This course comes with a full 30-day money-back guarantee. Meaning if you are not completely satisfied with the course or your progress, simply let me know and I’ll refund you 100%, every last penny no questions asked.

You either end up with Dimensionality Reduction skills, go on to develop great programs and potentially make an awesome career for yourself, or you try the course and simply get all your money back if you don’t like it…

You literally can’t lose.

Moreover, the course is packed with practical exercises that are based on real-life case studies. So not only will you learn the theory, but you will also get lots of hands-on practice building your own models.

And as a bonus, this course includes Python code templates which you can download and use on your own projects.

Ready to get started, developer?

Enroll now using the “Add to Cart” button on the right, and get started on your way to creative, advanced Data Science brilliance. Or, take this course for a free spin using the preview feature, so you know you’re 100% certain this course is for you.

See you on the inside (hurry, Dimensionality Reduction is waiting!)

Who this course is for:

Any people who want to start learning Dimensionality Reduction in Machine Learning
Anyone interested in Machine Learning
Students who have at least high school knowledge in math and who want to start learning Machine Learning.
Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets
Any students in college who want to start a career in Data Science
Any people who are not satisfied with their job and who want to become a Data Scientist
Any data analysts who want to level up in Machine Learning
Any people who want to create added value to their business by using powerful Machine Learning tools

Dimensionality Reduction: Machine Learning in Python

What you'll learn

Explore related topics

Course content

Code Environment Setup1 lecture • 3min

Machine Learning Fundamentals1 lecture • 4min

Principal Component Analysis (PCA)5 lectures • 2hr 21min

Locally Linear Embedding (LLE)5 lectures • 1hr 13min

t-Stochastic Neighbor Embedding (t-SNE)6 lectures • 1hr 29min

Multidimensional Scaling (MDS)3 lectures • 43min

ISOMAP3 lectures • 1hr

Fisher Discriminant Analysis5 lectures • 1hr 9min

Final Project - Images6 lectures • 1hr 50min

The Complete Machine Learning Course1 lecture • 1min

Requirements

Description

Who this course is for: