Visualization: Machine Learning in Python

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size4.1 GB
  • Uploaded Bytutsnode
  • Downloads86
  • Last checkedApr. 03rd '23
  • Date uploadedApr. 02nd '23
  • Seeders 60
  • Leechers68
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Infohash : 0A7366C81EA84E41A45CCAE6B07BF97EE222DAB2


Description

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Like you, thousands of others were frustrated and fed up with fragmented Youtube tutorials or incomplete or outdated courses which assume you already know a bunch of stuff, as well as thick, college-like textbooks able to send even the most caffeine-fuelled coder to sleep.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

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.

Last Updated 2/2023

Files:

Visualization Machine Learning in Python [TutsNode.net] - Visualization Machine Learning in Python 2 - Principal Component Analysis PCA
  • 5 - Using PCA.mp4 (461.6 MB)
  • 3 - Introduction-to-the-Dataset.ipynb (43.9 KB)
  • 4 - Initial Visualization.mp4 (217.3 MB)
  • 4 - Initial-Visualization.ipynb (719.2 KB)
  • 5 - Using-PCA.pdf (388.9 KB)
  • 5 - PCA-in-Crabs-Dataset.ipynb (986.5 KB)
  • 3 - crabs.csv (6.0 KB)
  • 3 - Introduction to the Dataset.mp4 (166.3 MB)
  • 6 - Principal-Component-Analysis-PCA.pdf (567.9 KB)
  • 4 - Initial-Visualization.pdf (243.9 KB)
  • 6 - Explanation of PCA.mp4 (98.2 MB)
  • 2 - Introduction-to-PCA.pdf (194.4 KB)
  • 3 - Introduction-to-the-Dataset.pdf (168.0 KB)
  • 2 - Introduction to PCA.mp4 (19.9 MB)
4 - tStochastic Neighbor Embedding tSNE
  • 16 - Using-t-SNE-with-Scaled-Data.ipynb (395.5 KB)
  • 15 - tSNE on Raw Data.mp4 (256.7 MB)
  • 16 - Using-t-SNE-on-Scaled-Data.pdf (180.8 KB)
  • 17 - Using-t-SNE-with-Standardized-Data.ipynb (583.6 KB)
  • 14 - crabs.csv (6.0 KB)
  • 14 - Introduction-to-the-Dataset.ipynb (11.1 KB)
  • 17 - Using-t-SNE-on-Standardized-Data.pdf (182.7 KB)
  • 12 - Introduction-to-t-SNE.pdf (374.7 KB)
  • 17 - tSNE on Standardized Data.mp4 (146.6 MB)
  • 16 - tSNE on Scaled Data.mp4 (145.4 MB)
  • 14 - Introduction to the Dataset.mp4 (84.9 MB)
  • 12 - Introduction to tSNE.mp4 (35.5 MB)
  • 15 - Using-t-SNE-with-Raw-Data.ipynb (192.8 KB)
  • 15 - Using-t-SNE-on-Raw-Data.pdf (182.0 KB)
  • 14 - Introduction-to-the-Dataset.pdf (163.2 KB)
  • 13 - Dataset.mp4 (3.0 MB)
8 - Final Project Images
  • 31 - Locally-Linear-Embedding.pdf (308.3 KB)
  • 30 - Introduction-to-Image-Dataset.ipynb (22.2 KB)
  • 33 - Fisher-Discriminant-Analysis.ipynb (316.7 KB)
  • 33 - Fisher Discriminant Analysis.mp4 (182.8 MB)
  • 31 - Locally Linear Embedding.mp4 (165.3 MB)
  • 34 - ISOMAP-Image-Project.ipynb (306.5 KB)
  • 32 - Principal Component Analysis.mp4 (164.8 MB)
  • 34 - ISOMAP.mp4 (151.1 MB)
  • 32 - Principal-Component-Analysis.ipynb (347.3 KB)
  • 34 - ISOMAP-Image-Project.pdf (303.8 KB)
  • 32 - Principal-Component-Analysis.pdf (303.7 KB)
  • 33 - Fisher-Discriminant-Analysis.pdf (303.2 KB)
  • 30 - Introduction-to-Image-Dataset.pdf (302.8 KB)
  • 31 - Locally-Linear-Embedding.ipynb (222.0 KB)
  • 30 - Introduction to Image Dataset.mp4 (91.3 MB)
  • 29 - Images.mp4 (2.0 MB)
6 - ISOMAP
  • 23 - ISOMAP with 3 Dimensions.mp4 (247.8 MB)
  • 22 - ISOMAP with 2 Dimensions.mp4 (214.1 MB)
  • 23 - ISOMAP-with-3-Dimensions.pdf (160.2 KB)
  • 22 - ISOMAP-with-2-Dimensions.ipynb (90.1 KB)
  • 23 - ISOMAP-with-3-Dimensions.ipynb (224.8 KB)
  • 21 - Introduction-to-ISOMAP.pdf (164.3 KB)
  • 22 - ISOMAP-with-2-Dimensions.pdf (160.0 KB)
  • 21 - Introduccion to ISOMAP.mp4 (17.5 MB)
7 - Fisher Discriminant Analysis
  • 27 - Fisher-Discriminant-Analysis-with-2-Dimensions.ipynb (97.4 KB)
  • 27 - Fisher Discriminant Analysis with 2 Dimensions.mp4 (209.6 MB)
  • 26 - Introduction-to-the-Dataset.ipynb (11.1 KB)
  • 28 - Fisher Discriminant Analysis with 3 Dimensions.mp4 (188.0 MB)
  • 28 - Fisher-Discriminant-Analysis-with-3-Dimensions.pdf (251.0 KB)
  • 27 - Fisher-Discriminant-Analysis-with-2-Dimensions.pdf (250.9 KB)
  • 28 - Fisher-Discriminant-Analysis-with-3-Dimensions.ipynb (216.0 KB)
  • 26 - Introduction to the Dataset.mp4 (85.0 MB)
  • 24 - Introduction-to-Fisher-Discriminant-Analysis.pdf (196.7 KB)
  • 26 - Introduction-to-the-Dataset.pdf (163.2 KB)
  • 24 - Introduction to Fisher Discriminant Analysis.mp4 (12.7 MB)
  • 25 - Dataset Information.mp4 (3.0 MB)
3 - Locally Linear Embedding LLE
  • 9 - Introduction-to-the-Dataset.ipynb (11.1 KB)
  • 11 - LLE with 3 Dimensions.mp4 (201.8 MB)
  • 10 - Using LLE.mp4 (200.1 MB)
  • 9 - crabs.csv (6.0 KB)
  • 10 - Using-LLE.pdf (199.8 KB)
  • 8 - Locally-Linear-Embedding-Algorithm.pdf (296.0 KB)
  • 10 - Using-Locally-Linear-Embedding.ipynb (83.7 KB)
  • 11 - LLE-with-3-Dimensions.pdf (237.1 KB)
  • 7 - Introduction-to-LLE.pdf (237.0 KB)
  • 11 - LLE-with-3-Dimensions.ipynb (218.6 KB)
  • 9 - Introduction to the Dataset.mp4 (84.9 MB)
  • 7 - Introduction to LLE.mp4 (19.6 MB)
  • 9 - Introduction-to-the-Dataset.pdf (163.2 KB)
  • 8 - Locally Linear Embedding Algorithm.mp4 (19.2 MB)
5 - Multidimensional Scaling MDS
  • 18 - Introduction-to-Multidimensional-Scaling.pdf (164.5 KB)
  • 20 - MDS-with-3-Dimensions.ipynb (230.7 KB)
  • 19 - Using MDS with 2 Dimensions.mp4 (150.1 MB)
  • 19 - MDS-with-2-Dimensions.ipynb (89.6 KB)
  • 20 - Using MDS with 3 Dimensions.mp4 (143.7 MB)
  • 18 - Introduction to MDS.mp4 (21.2 MB)
  • 19 - Multidimensional-Scaling-with-2-Dimensions.pdf (158.9 KB)
  • 20 - Multidimensional-Scaling-with-3-Dimensions.pdf (158.8 KB)
1 - Code Environment Setup
  • 1 - Google-Colab-for-Programming-in-Python.pdf (577.8 KB)
  • 1 - Google Colab for Programming in Python.mp4 (9.6 MB)
  • TutsNode.net.txt (0.1 KB)
  • [TGx]Downloaded from torrentgalaxy.to .txt (0.6 KB)
    • .pad
    • 0 (1.8 KB)
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