Artem Ryblov’s Data Science Weekly
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@artemfisherman’s Data Science Weekly: Elevate your expertise with a standout data science resource each week, carefully chosen for depth and impact.

Long-form content: https://artemryblov.substack.com
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End to End Machine Learning (FREE Courses)

The best way to learn new concepts is to use them to build something. These courses are structured to build foundational knowledge (100 series), provide in-depth applied machine learning case studies (200 series), and embark on project-driven deep-dives (300 series).

- 111. Getting ready to learn Python, Mac edition
- 112. Getting ready to learn Python, Windows edition
- 201. Intro to Python
- 211. Decision Trees with Python and Pandas
- 212. Time-Series Analysis
- 213. Nonlinear Modelling and Optimization
- 221. The k-nearest neighbours algorithm
- 311. Neural Network Visualization
- 312. Build a Neural Network Framework
- 313. Advanced Neural Network Methods
- 314. Neural Network Optimization
- 321. Convolutional Neural Networks in One Dimension
- 322. Convolutional neural networks in two dimensions

Come have a look around and try one out today!

Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #machinelearning #ml #algorithms #learning #course #python #decisiontrees #pandas #timeseries #nonlinear #knn #neuralnetworks #neuralnetwork #convolutionalneuralnetworks #optimization #analysis #visualization

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Stanford CS 230 ― Deep Learning

Set of illustrated Deep Learning cheatsheets covering the content of the CS 230 class.

They can (hopefully!) be useful to all future students of this course as well as to anyone else interested in Deep Learning.

Link: https://stanford.edu/~shervine/teaching/cs-230/

Navigational hashtags: #armknowledgesharing #armcheetsheets
General hashtags: #machinelearning #students #content #deeplearning #tips #tricks #cheetsheet #convolutionalneuralnetworks #recurrentneuralnetworks

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