Graph ML and deep learning courses
This is another post on your request. Other courses you requested will be shared in following days.
Geometric Deep learning course
AMMI21
π¨βπ« Teachers: Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar VeliΔkoviΔ
π12 lectures, 2 tutorials, and 4 seminars
This course follows GDL BOOK
π Course link: https://geometricdeeplearning.com/lectures/
Machine Learning for Graphs and Sequential Data (MLGS)
by Stephan GΓΌnnemann
Awesome course covering in depth generative models, robustness, sequential data, clustering, label propagation, GNNs, and more
π Course link: https://www.in.tum.de/daml/teaching/mlgs/
Stanford CS224W course on graph ML
A legendary Stanford CS224W course on graph ML now releases videos on YouTube for 2021
π¬ 60 Videos
β° 30h
π Course link
Python For Data Science (Udemy)
This course specifically created for Data Science / AI / ML / DL. It covers BASICS PYTHON ONLY
Rating βοΈ: 4.1 out of 5
Students π¨βπ: 65,523 students
Duration β°: 3hr 55min of on-demand video
π Course link
Deep Learning Prerequisites: The Numpy Stack in Python V2 (Udemy)
Rating βοΈ: 4.6 out of 5
Students π¨βπ: 34,785
Duration β°: 1hr 59min of on-demand video
π Course link
There is also this cool blogpost by GordiΔ Aleksa:
How to get started with Graph Machine Learning
And one early access version book:
Graph Powered Machine Learning
by: Allesandro Negro
π Book link
#graphML #ML #machinelearning #deeplearning #python
ββββββββββββββ
πJoin @bigdataspecialist for moreπ
This is another post on your request. Other courses you requested will be shared in following days.
Geometric Deep learning course
AMMI21
π¨βπ« Teachers: Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar VeliΔkoviΔ
π12 lectures, 2 tutorials, and 4 seminars
This course follows GDL BOOK
π Course link: https://geometricdeeplearning.com/lectures/
Machine Learning for Graphs and Sequential Data (MLGS)
by Stephan GΓΌnnemann
Awesome course covering in depth generative models, robustness, sequential data, clustering, label propagation, GNNs, and more
π Course link: https://www.in.tum.de/daml/teaching/mlgs/
Stanford CS224W course on graph ML
A legendary Stanford CS224W course on graph ML now releases videos on YouTube for 2021
π¬ 60 Videos
β° 30h
π Course link
Python For Data Science (Udemy)
This course specifically created for Data Science / AI / ML / DL. It covers BASICS PYTHON ONLY
Rating βοΈ: 4.1 out of 5
Students π¨βπ: 65,523 students
Duration β°: 3hr 55min of on-demand video
π Course link
Deep Learning Prerequisites: The Numpy Stack in Python V2 (Udemy)
Rating βοΈ: 4.6 out of 5
Students π¨βπ: 34,785
Duration β°: 1hr 59min of on-demand video
π Course link
There is also this cool blogpost by GordiΔ Aleksa:
How to get started with Graph Machine Learning
And one early access version book:
Graph Powered Machine Learning
by: Allesandro Negro
π Book link
#graphML #ML #machinelearning #deeplearning #python
ββββββββββββββ
πJoin @bigdataspecialist for moreπ
Geometricdeeplearning
GDL Course
Grids, Groups, Graphs, Geodesics, and Gauges
Deep Learning Do It Yourself!
This site collects resources to learn Deep Learning in the form of Modules available through the sidebar on the left.
https://dataflowr.github.io/website/
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*This channel belongs to @bigdataspecialist group
This site collects resources to learn Deep Learning in the form of Modules available through the sidebar on the left.
https://dataflowr.github.io/website/
ββββββββββββββ
Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
Forwarded from Coding interview preparation
π Book link
#machinelearning #ml #datascience
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*This channel belongs to @bigdataspecialist group
#machinelearning #ml #datascience
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Lectures for UC Berkeley CS 182: Deep Learning
Spring 2021
π¬ 66 videos
β° 26 hours
https://www.youtube.com/playlist?list=PL_iWQOsE6TfVmKkQHucjPAoRtIJYt8a5A
#deeplearning
ββββββββββββββ
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*This channel belongs to @bigdataspecialist group
Spring 2021
π¬ 66 videos
β° 26 hours
https://www.youtube.com/playlist?list=PL_iWQOsE6TfVmKkQHucjPAoRtIJYt8a5A
#deeplearning
ββββββββββββββ
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*This channel belongs to @bigdataspecialist group
YouTube
Deep Learning: CS 182 Spring 2021
Lectures for UC Berkeley CS 182: Deep Learning.
The Incredible PyTorch
A curated list of tutorials, papers, projects, communities and more relating to PyTorch.
https://www.ritchieng.com/the-incredible-pytorch/
#pytorch
ββββββββββββββ
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*This channel belongs to @bigdataspecialist group
A curated list of tutorials, papers, projects, communities and more relating to PyTorch.
https://www.ritchieng.com/the-incredible-pytorch/
#pytorch
ββββββββββββββ
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*This channel belongs to @bigdataspecialist group
FOUNDATIONS OF MACHINE LEARNING
by Bloomberg
Understand the Concepts, Techniques and Mathematical Frameworks Used by Experts in Machine Learning
π¬ 30 video lessons with slides
β° 28 hours
https://bloomberg.github.io/foml/#home
#machinelearning #ml
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by Bloomberg
Understand the Concepts, Techniques and Mathematical Frameworks Used by Experts in Machine Learning
π¬ 30 video lessons with slides
β° 28 hours
https://bloomberg.github.io/foml/#home
#machinelearning #ml
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*This channel belongs to @bigdataspecialist group
Intro to Machine Learning
by Kaggle
Learn the core ideas in machine learning, and build your first models.
1 How Models Work
The first step if you're new to machine learning.
2 Basic Data Exploration
Load and understand your data.
3 Your First Machine Learning Model
Building your first model. Hurray!
4 Model Validation
Measure the performance of your model, so you can test and compare alternatives.
5 Underfitting and Overfitting
Fine-tune your model for better performance.
6 Random Forests
Using a more sophisticated machine learning algorithm.
7 Machine Learning Competitions
Enter the world of machine learning competitions to keep improving and see your progress.
π Course link
#machinelearning #ml
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by Kaggle
Learn the core ideas in machine learning, and build your first models.
1 How Models Work
The first step if you're new to machine learning.
2 Basic Data Exploration
Load and understand your data.
3 Your First Machine Learning Model
Building your first model. Hurray!
4 Model Validation
Measure the performance of your model, so you can test and compare alternatives.
5 Underfitting and Overfitting
Fine-tune your model for better performance.
6 Random Forests
Using a more sophisticated machine learning algorithm.
7 Machine Learning Competitions
Enter the world of machine learning competitions to keep improving and see your progress.
π Course link
#machinelearning #ml
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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
Kaggle
Learn Intro to Machine Learning Tutorials
Learn the core ideas in machine learning, and build your first models.
Reproducible Data Science with Docker
https://archive.org/details/pyconza2018-Reproducible_Data_Science_with_Docker
https://archive.org/details/pyconza2018-Reproducible_Data_Science_with_Docker
Internet Archive
Reproducible Data Science with Docker : Richard Ackon : Free Download, Borrow, and Streaming : Internet Archive
Richard Ackon https://2018.za.pycon.org/talks/48-reproducible-data-science-with-docker/ Collaboration is a major part of doing Data Science. This means Data...
The R Programming For Data Science A-Z Complete Diploma 2022
Rating βοΈ: 4.5 out of 5
Students π¨βπ: 38,584
Duration β°: 5h 6min
π Course link
Free for first 1000 enrollments
Rating βοΈ: 4.5 out of 5
Students π¨βπ: 38,584
Duration β°: 5h 6min
π Course link
Free for first 1000 enrollments
Udemy
Data Science: R Programming Complete Diploma 2023
Learn all the R skills you need to become a Professional and Certified R Programmer with this Complete Bootcamp
Forwarded from Free programming books
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Knowledge Graphs Course
Data Models, Knowledge Acquisition, Inference and Applications
Department of Computer Science, Stanford University, Spring 2021
β³10 weeks, each week has slides and video lessons π½
https://web.stanford.edu/class/cs520/
#datascience #machinelearning #tensorflow #scikitlearn #keras
βββββββββββββ
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Data Models, Knowledge Acquisition, Inference and Applications
Department of Computer Science, Stanford University, Spring 2021
β³10 weeks, each week has slides and video lessons π½
https://web.stanford.edu/class/cs520/
#datascience #machinelearning #tensorflow #scikitlearn #keras
βββββββββββββ
Join @programming_books_bds for more