I'm thrilled to announce the latest release of MetaTS, a #Python package that simplifies and accelerates global #timeseries #forecasting using #meta-learning.
You can find MetaTS on GitHub here: https://github.com/DrSasanBarak/metats
Meta-learning has emerged as a winning solution for recent time series #forecasting competitions, and MetaTS is designed to make #meta-learning more accessible to researchers and data scientists. With MetaTS, you can easily generate meta-features using automated feature extraction and deep unsupervised learning, implement base-forecaster models, and optimize meta-parameters using a flexible and customizable pipeline.
In addition to providing a user-friendly toolkit for meta-learning, MetaTS also unifies the available Python libraries that can be useful for time series forecasting. You can leverage the power of #Sktime, #Nixtla, #Darts, and other libraries to create base forecasters and explore different meta-model architectures, including #stacking and #ensembling.
I'm proud of what we've achieved with MetaTS, and I believe it can be a valuable resource for anyone looking to improve their time series forecasting #performance. The latest version of the package is available on GitHub, and we welcome any feedback or contributions to help make MetaTS even better.
This can not be done without a great dedication and contribution of my colleague @AmirabbasAsadi .
Be tuned about this project on my LinkedIn
Thank you for your support!
You can find MetaTS on GitHub here: https://github.com/DrSasanBarak/metats
Meta-learning has emerged as a winning solution for recent time series #forecasting competitions, and MetaTS is designed to make #meta-learning more accessible to researchers and data scientists. With MetaTS, you can easily generate meta-features using automated feature extraction and deep unsupervised learning, implement base-forecaster models, and optimize meta-parameters using a flexible and customizable pipeline.
In addition to providing a user-friendly toolkit for meta-learning, MetaTS also unifies the available Python libraries that can be useful for time series forecasting. You can leverage the power of #Sktime, #Nixtla, #Darts, and other libraries to create base forecasters and explore different meta-model architectures, including #stacking and #ensembling.
I'm proud of what we've achieved with MetaTS, and I believe it can be a valuable resource for anyone looking to improve their time series forecasting #performance. The latest version of the package is available on GitHub, and we welcome any feedback or contributions to help make MetaTS even better.
This can not be done without a great dedication and contribution of my colleague @AmirabbasAsadi .
Be tuned about this project on my LinkedIn
Thank you for your support!
GitHub
GitHub - DrSasanBarak/metats: MetaTS | Time Series Forecasting using Meta Learning
MetaTS | Time Series Forecasting using Meta Learning - DrSasanBarak/metats
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#Python Course πππ
FreeCodeCamp released a course from Harvard University - Introduction to Programming with Python by Prof. David J. Malan. The course is for beginners with no previous programming experience focusing on the following topics:
β Functions and variables
β Conditionals and loops
β Libraries and unit tests
β Regular expressions
β Object-oriented programming
Resources π
β‘οΈ Course: https://www.youtube.com/watch?v=nLRL_NcnK-4&ab_channel=freeCodeCamp.org
β‘οΈ Slides: https://cs50.harvard.edu/python/2022/
Thanks to the freeCodeCamp β€οΈ for keep creating and sharing great content!
FreeCodeCamp released a course from Harvard University - Introduction to Programming with Python by Prof. David J. Malan. The course is for beginners with no previous programming experience focusing on the following topics:
β Functions and variables
β Conditionals and loops
β Libraries and unit tests
β Regular expressions
β Object-oriented programming
Resources π
β‘οΈ Course: https://www.youtube.com/watch?v=nLRL_NcnK-4&ab_channel=freeCodeCamp.org
β‘οΈ Slides: https://cs50.harvard.edu/python/2022/
Thanks to the freeCodeCamp β€οΈ for keep creating and sharing great content!
YouTube
Harvard CS50βs Introduction to Programming with Python β Full University Course
Learn Python programming from Harvard University. It dives more deeply into the design and implementation of web apps with Python, JavaScript, and SQL using frameworks like Django, React, and Bootstrap. Topics include database design, scalability, securityβ¦
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Advanced Python Course πππ
FreeCodeCamp released a course - Create a Programming Language and Learn Advanced Python, by Aryaan Hegde. The course focuses on advanced topics in Python, such as:
β Object-oriented programming
β Data structure
β Recursion
β Building algorithms
Video: https://www.youtube.com/watch?v=1WpKsY9LBlY&t=38s&ab_channel=freeCodeCamp.org
#python #computerscience #datascience #dataengineering
FreeCodeCamp released a course - Create a Programming Language and Learn Advanced Python, by Aryaan Hegde. The course focuses on advanced topics in Python, such as:
β Object-oriented programming
β Data structure
β Recursion
β Building algorithms
Video: https://www.youtube.com/watch?v=1WpKsY9LBlY&t=38s&ab_channel=freeCodeCamp.org
#python #computerscience #datascience #dataengineering
YouTube
Create a Programming Language and Learn Advanced Python β Full Course
Make your own programming language while learning advanced Python.
βοΈ Course from Aryaan Hegde.
Aryaan's website: https://blog.algolearn.net/
π» Code: https://github.com/VOYAGERX013/ShadowScript
βοΈ Contents βοΈ
(0:00:00) Intro
(0:07:05) Logic gates
(0:12:14)β¦
βοΈ Course from Aryaan Hegde.
Aryaan's website: https://blog.algolearn.net/
π» Code: https://github.com/VOYAGERX013/ShadowScript
βοΈ Contents βοΈ
(0:00:00) Intro
(0:07:05) Logic gates
(0:12:14)β¦
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Introduction to Machine Learning with Python and Scikit-Learn Course πππ
Another FreeCodeCamp course recently published for machine learning with Python. This is a long one (18 hours) that provides an in-depth introduction to machine learning and covers topics such as:
β Linear Regression and Gradient Descent
β Logistic Regression for Classification
β Decision Trees and Random Forests
β Gradient Boosting Machines with XGBoost
β Unsupervised Learning using Scikit-Learn
β Machine Learning Project from Scratch
β Deploying a Machine Learning Project with Flask
Course π½οΈ: https://www.youtube.com/watch?v=hDKCxebp88A
#datascience #python #machinelearning #scikitlearn
Another FreeCodeCamp course recently published for machine learning with Python. This is a long one (18 hours) that provides an in-depth introduction to machine learning and covers topics such as:
β Linear Regression and Gradient Descent
β Logistic Regression for Classification
β Decision Trees and Random Forests
β Gradient Boosting Machines with XGBoost
β Unsupervised Learning using Scikit-Learn
β Machine Learning Project from Scratch
β Deploying a Machine Learning Project with Flask
Course π½οΈ: https://www.youtube.com/watch?v=hDKCxebp88A
#datascience #python #machinelearning #scikitlearn
YouTube
Machine Learning with Python and Scikit-Learn β Full Course
This course is a practical and hands-on introduction to Machine Learning with Python and Scikit-Learn for beginners with basic knowledge of Python and statistics.
It is designed and taught by Aakash N S, CEO and co-founder of Jovian. Check out their YouTubeβ¦
It is designed and taught by Aakash N S, CEO and co-founder of Jovian. Check out their YouTubeβ¦
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