| 1 - Google Colab for Programming in Python.mp4 | 6.8 MB | ||
| 1 - Google-Colab-for-Programming-in-Python.pdf | 577.8 KB | ||
| 2 - Introduction to Machine Learning.mp4 | 10.6 MB | ||
| 2 - Introduction-to-Machine-Learning.pdf | 284.7 KB | ||
| 3 - Energy.csv | 4.3 MB | ||
| 3 - Introduction to the Dataset.mp4 | 93.8 MB | ||
| 3 - Introduction-to-the-Dataset.ipynb | 48.4 KB | ||
| 3 - Introduction-to-the-Dataset.pdf | 362.1 KB | ||
| 4 - Partition of the Dataset Time Series Windows.mp4 | 120.4 MB | ||
| 4 - Partition-of-the-Dataset-Time-Series-Windows.ipynb | 52.4 KB | ||
| 4 - Partition-of-the-Dataset-Time-Series-Windows.pdf | 491.9 KB | ||
| 5 - Partition of the Dataset Target Variable.mp4 | 37 MB | ||
| 5 - Partition-of-the-Dataset-Target-Variable.ipynb | 49.4 KB | ||
| 5 - Partition-of-the-Dataset-Target-Variable.pdf | 424.9 KB | ||
| 6 - SVM-Linear-Kernel.ipynb | 157 KB | ||
| 6 - Support Vector Machine Linear Kernel.mp4 | 194.4 MB | ||
| 6 - Support-Vector-Machine-Linear-Kernel.pdf | 516.3 KB | ||
| 7 - SVM-Polynomial-Kernels.ipynb | 257.7 KB | ||
| 7 - Support Vector Machines Polynomial Kernels.mp4 | 72.1 MB | ||
| 7 - Support-Vector-Machine-Polynomial-Kernels.pdf | 742.4 KB | ||
| 8 - SVM-RBF-Kernel.ipynb | 359.8 KB | ||
| 8 - Support Vector Machine Radial Basis Function RBF Kernel.mp4 | 123.5 MB | ||
| 8 - Support-Vector-Machine-RBF-Kernel.pdf | 537.8 KB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 25 total files | |||
Support Vector Machines For Regression: Machine Learning
https://DevCourseWeb.com
Last updated 1/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English (US) | Size: 667.61 MB | Duration: 1h 38m
Learn to use Support Vector Machines for Regression from a Data Science expert. Code templates included.
What you'll learn
Master Support Vector Machines for Regression in Python
Become an advanced, confident, and modern data scientist from scratch
Become job-ready by understanding how Support Vector Machines really work behind the scenes
Apply robust Data Science techniques for Support Vector Machines
How to think and work like a data scientist: problem-solving, researching, workflows
Get fast and friendly support in the Q&A area
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.
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