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Dimensionality Reduction: Machine Learning in Python

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Dimensionality Reduction: Machine Learning in Python, available at $54.99, has an average rating of 4.7, with 35 lectures, based on 10 reviews, and has 835 subscribers.

You will learn about 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) This course is ideal for individuals who are Any people who want to start learning Dimensionality Reduction in Machine Learning or Anyone interested in Machine Learning or Students who have at least high school knowledge in math and who want to start learning Machine Learning. or Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets or Any students in college who want to start a career in Data Science or Any people who are not satisfied with their job and who want to become a Data Scientist or Any data analysts who want to level up in Machine Learning or Any people who want to create added value to their business by using powerful Machine Learning tools It is particularly useful for Any people who want to start learning Dimensionality Reduction in Machine Learning or Anyone interested in Machine Learning or Students who have at least high school knowledge in math and who want to start learning Machine Learning. or Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets or Any students in college who want to start a career in Data Science or Any people who are not satisfied with their job and who want to become a Data Scientist or Any data analysts who want to level up in Machine Learning or Any people who want to create added value to their business by using powerful Machine Learning tools.

Enroll now: Dimensionality Reduction: Machine Learning in Python

Summary

Title: Dimensionality Reduction: Machine Learning in Python

Price: $54.99

Average Rating: 4.7

Number of Lectures: 35

Number of Published Lectures: 35

Number of Curriculum Items: 35

Number of Published Curriculum Objects: 35

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will 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)

Who Should Attend

  • 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

Target Audiences

  • 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

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 templateswhich you can download and use on your own projects.

Ready to get started, developer?

Enroll nowusing 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!)

Course Curriculum

Chapter 1: Code Environment Setup

Lecture 1: Google Colab for Programming in Python

Chapter 2: Machine Learning Fundamentals

Lecture 1: Introduction to Machine Learning

Chapter 3: Principal Component Analysis (PCA)

Lecture 1: Introduction to PCA

Lecture 2: Introduction to the Dataset

Lecture 3: Initial Visualization

Lecture 4: Using PCA

Lecture 5: Explanation of PCA

Chapter 4: Locally Linear Embedding (LLE)

Lecture 1: Introduction to LLE

Lecture 2: Locally Linear Embedding Algorithm

Lecture 3: Introduction to the Dataset

Lecture 4: Using LLE

Lecture 5: LLE with 3 Dimensions

Chapter 5: t-Stochastic Neighbor Embedding (t-SNE)

Lecture 1: Introduction to t-SNE

Lecture 2: Dataset

Lecture 3: Introduction to the Dataset

Lecture 4: t-SNE on Raw Data

Lecture 5: t-SNE on Scaled Data

Lecture 6: t-SNE on Standardized Data

Chapter 6: Multidimensional Scaling (MDS)

Lecture 1: Introduction to MDS

Lecture 2: Using MDS with 2 Dimensions

Lecture 3: Using MDS with 3 Dimensions

Chapter 7: ISOMAP

Lecture 1: Introducción to ISOMAP

Lecture 2: ISOMAP with 2 Dimensions

Lecture 3: ISOMAP with 3 Dimensions

Chapter 8: Fisher Discriminant Analysis

Lecture 1: Introduction to Fisher Discriminant Analysis

Lecture 2: Dataset Information

Lecture 3: Introduction to the Dataset

Lecture 4: Fisher Discriminant Analysis with 2 Dimensions

Lecture 5: Fisher Discriminant Analysis with 3 Dimensions

Chapter 9: Final Project – Images

Lecture 1: Images

Lecture 2: Introduction to Image Dataset

Lecture 3: Locally Linear Embedding

Lecture 4: Principal Component Analysis

Lecture 5: Fisher Discriminant Analysis

Lecture 6: ISOMAP

Instructors

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  • 4 stars: 4 votes
  • 5 stars: 6 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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