The Complete Machine Learning Course: From Zero to Expert!

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The Complete Machine Learning Course From Zero to Expert [TutsNode.net] - The Complete Machine Learning Course From Zero to Expert 2 - Visualization Principal Component Analysis
  • 5 - Using PCA.mp4 (464.3 MB)
  • 4 - Initial-Visualization.ipynb (719.2 KB)
  • 3 - Introduction-to-the-Dataset.ipynb (43.9 KB)
  • 4 - Initial Visualization.mp4 (218.6 MB)
  • 5 - Using-PCA.pdf (388.9 KB)
  • 5 - Using PCA English.srt (90.3 KB)
  • 4 - Initial Visualization English.srt (41.2 KB)
  • 3 - Introduction to the Dataset.mp4 (167.4 MB)
  • 3 - crabs.csv (6.0 KB)
  • 4 - Initial-Visualization.pdf (243.9 KB)
  • 3 - Introduction to the Dataset English.srt (30.8 KB)
  • 2 - Introduction-to-PCA.pdf (194.4 KB)
  • 4 - crabs.csv (6.0 KB)
  • 3 - Introduction-to-the-Dataset.pdf (168.0 KB)
  • 2 - Introduction to PCA English.srt (5.3 KB)
  • 2 - Introduction to PCA.mp4 (20.1 MB)
  • 5 - PCA-in-Crabs-Dataset.ipynb (986.5 KB)
10 - Ridge Regression
  • 36 - Ridge Regression and Cross Validation.mp4 (326.9 MB)
  • 36 - Ridge Regression and Cross Validation English.srt (54.8 KB)
  • 36 - Ridge-Regression.pdf (186.4 KB)
7 - Visualization Fisher Discriminant Analysis
  • 26 - Fisher-Discriminant-Analysis-with-2-Dimensions.ipynb (97.4 KB)
  • 23 - Introduction to Fisher Discriminant Analysis English.srt (3.1 KB)
  • 24 - Dataset Information English.srt (0.9 KB)
  • 26 - Fisher Discriminant Analysis with 2 Dimensions.mp4 (210.8 MB)
  • 25 - Introduction-to-the-Dataset.ipynb (11.1 KB)
  • 27 - Fisher Discriminant Analysis with 3 Dimensions.mp4 (189.0 MB)
  • 27 - Fisher-Discriminant-Analysis-with-3-Dimensions.pdf (251.0 KB)
  • 25 - crabs.csv (6.0 KB)
  • 27 - Fisher Discriminant Analysis with 3 Dimensions English.srt (32.7 KB)
  • 26 - Fisher-Discriminant-Analysis-with-2-Dimensions.pdf (250.9 KB)
  • 27 - Fisher-Discriminant-Analysis-with-3-Dimensions.ipynb (216.0 KB)
  • 26 - Fisher Discriminant Analysis with 2 Dimensions English.srt (36.7 KB)
  • 25 - Introduction to the Dataset.mp4 (85.5 MB)
  • 25 - Introduction-to-the-Dataset.pdf (163.2 KB)
  • 25 - Introduction to the Dataset English.srt (21.0 KB)
  • 23 - Introduction to Fisher Discriminant Analysis.mp4 (12.8 MB)
  • 24 - Dataset Information.mp4 (3.0 MB)
3 - Visualization Locally Linear Embedding LLE
  • 8 - Introduction-to-the-Dataset.ipynb (11.1 KB)
  • 10 - LLE with 3 Dimensions English.srt (32.7 KB)
  • 10 - LLE with 3 Dimensions.mp4 (202.9 MB)
  • 9 - Using LLE.mp4 (201.3 MB)
  • 8 - crabs.csv (6.0 KB)
  • 9 - crabs.csv (6.0 KB)
  • 10 - crabs.csv (6.0 KB)
  • 7 - Locally-Linear-Embedding-Algorithm.pdf (296.0 KB)
  • 10 - LLE-with-3-Dimensions.pdf (237.1 KB)
  • 9 - Using-LLE.pdf (199.8 KB)
  • 6 - Introduction-to-LLE.pdf (237.0 KB)
  • 10 - LLE-with-3-Dimensions.ipynb (218.6 KB)
  • 9 - Using LLE English.srt (40.2 KB)
  • 9 - Using-Locally-Linear-Embedding.ipynb (83.7 KB)
  • 8 - Introduction-to-the-Dataset.pdf (163.2 KB)
  • 8 - Introduction to the Dataset English.srt (21.0 KB)
  • 7 - Locally Linear Embedding Algorithm English.srt (4.8 KB)
  • 6 - Introduction to LLE English.srt (4.0 KB)
  • 8 - Introduction to the Dataset.mp4 (85.5 MB)
  • 6 - Introduction to LLE.mp4 (19.7 MB)
  • 7 - Locally Linear Embedding Algorithm.mp4 (19.3 MB)
4 - Visualization tStochastic Neighbor Embedding tSNE
  • 14 - tSNE on Raw Data.mp4 (258.3 MB)
  • 16 - Using-t-SNE-with-Standardized-Data.ipynb (583.6 KB)
  • 16 - Using-t-SNE-on-Standardized-Data.pdf (182.7 KB)
  • 11 - Introduction to tSNE English.srt (6.5 KB)
  • 12 - Dataset English.srt (0.9 KB)
  • 15 - Using-t-SNE-with-Scaled-Data.ipynb (395.5 KB)
  • 14 - tSNE on Raw Data English.srt (50.2 KB)
  • 13 - Introduction-to-the-Dataset.ipynb (11.1 KB)
  • 13 - crabs.csv (6.0 KB)
  • 11 - Introduction-to-t-SNE.pdf (374.7 KB)
  • 14 - Using-t-SNE-with-Raw-Data.ipynb (192.8 KB)
  • 16 - tSNE on Standardized Data.mp4 (147.3 MB)
  • 15 - tSNE on Scaled Data.mp4 (146.1 MB)
  • 14 - Using-t-SNE-on-Raw-Data.pdf (182.0 KB)
  • 15 - Using-t-SNE-on-Scaled-Data.pdf (180.8 KB)
  • 13 - Introduction-to-the-Dataset.pdf (163.2 KB)
  • 16 - tSNE on Standardized Data English.srt (23.5 KB)
  • 13 - Introduction to the Dataset.mp4 (85.5 MB)
  • 15 - tSNE on Scaled Data English.srt (23.2 KB)
  • 11 - Introduction to tSNE.mp4 (35.6 MB)
  • 13 - Introduction to the Dataset English.srt (21.0 KB)
  • 12 - Dataset.mp4 (3.0 MB)
6 - Visualization ISOMAP
  • 22 - ISOMAP with 3 Dimensions.mp4 (248.9 MB)
  • 20 - Introduccion to ISOMAP English.srt (3.4 KB)
  • 21 - ISOMAP with 2 Dimensions.mp4 (215.3 MB)
  • 22 - ISOMAP-with-3-Dimensions.ipynb (224.8 KB)
  • 21 - ISOMAP with 2 Dimensions English.srt (40.5 KB)
  • 21 - ISOMAP-with-2-Dimensions.ipynb (90.1 KB)
  • 22 - ISOMAP with 3 Dimensions English.srt (38.8 KB)
  • 20 - Introduction-to-ISOMAP.pdf (164.3 KB)
  • 22 - ISOMAP-with-3-Dimensions.pdf (160.2 KB)
  • 21 - ISOMAP-with-2-Dimensions.pdf (160.0 KB)
  • 20 - Introduccion to ISOMAP.mp4 (17.6 MB)
9 - Linear Regression
  • 33 - Linear Regression.mp4 (224.1 MB)
  • 35 - Cross-Validation.ipynb (327.3 KB)
  • 31 - Introduction-to-the-Dataset.ipynb (15.5 KB)
  • 33 - Linear Regression English.srt (45.0 KB)
  • 31 - LifeExpectancy.csv (367.2 KB)
  • 34 - Metrics.ipynb (316.6 KB)
  • 33 - Linear-Regression.ipynb (303.5 KB)
  • 32 - Preprocessing.mp4 (160.7 MB)
  • 35 - Cross Validation.mp4 (156.5 MB)
  • 32 - Preprocessing.ipynb (31.3 KB)
  • 35 - Cross-Validation.pdf (245.1 KB)
  • 31 - Introduction to the Dataset.mp4 (100.4 MB)
  • 34 - Metrics.mp4 (97.5 MB)
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Description


Description

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

Whether you want to:

– build the skills you need to get your first data science job

– move to a more senior software developer position

– become a computer scientist mastering in data science

– or just learn Machine Learning to be able to create your own projects quickly.

…this complete Machine Learning Masterclass is the course you need to do all of this, and more.

This course is designed to give you the machine learning skills you need to become a data science expert. By the end of the course, you will understand the machine learning method extremely well and be able to apply it in your own data science projects and be productive as a computer scientist and developer.

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 Machine Learning 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 machine learning skills and master data science. It’s a one-stop shop to learn machine learning. If you want to go beyond the core content you can do so at any time.

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 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 machine learning 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 machine learning 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, Machine Learning is waiting!)
Who this course is for:

Anyone interested in Machine Learning
Any people who have been trying to learn Machine Learning but: 1) still don’t really understand it, or 2) still don’t feel confident to take a job interview
Any students in college who want to start a career in Data Science
Anyone interested in working as 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.
Anyone who wants to work as a Data Analyst in research, economics, finance, marketing, engineering or medical sectors

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 1/2023



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The Complete Machine Learning Course: From Zero to Expert!


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4.8 GB
seeders:71
leechers:80
The Complete Machine Learning Course: From Zero to Expert!


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