Dimensionality Reduction: Machine Learning with Python



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

What you will learn

Master Dimensionality Reduction on 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)

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 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Like you, thousands of others were frustrated and fed up with fragmented Youtube tutorials or incomplete or outdated courses which assume you already know a bunch of stuff, as well as thick, college-like textbooks able to send even the most caffeine-fuelled coder to sleep.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 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 Dimensionality Reduction techniques  and master data science. It’s a one-stop shop to learn 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 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!)

English
language

Content

Code Environment Setup

Google Colab for Programming in Python

Principal Component Analysis (PCA)

Introduction to PCA
Introduction to the Dataset
Initial Visualization
Using PCA
Explanation of PCA

Locally Linear Embedding (LLE)

Introduction to LLE
Locally Linear Embedding Algorithm
Introduction to the Dataset
Using LLE
LLE with 3 Dimensions

t-Stochastic Neighbor Embedding (t-SNE)

Introduction to the Dataset
t-SNE on Raw Data
t-SNE on Scaled Data
t-SNE on Standardized Data

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