https://hackernoon.com/absolute-fundamentals-of-machine-learning-dca5deee78df?gi=2c99287cb9f5
#machine_learning , what a buzzword. I’m sure you all want to understand machine learning, and that’s what I’m going to teach in this article.
I found that learning the theroetical side alongside the programming side makes it easier to learn both, so this article features both easy to understand mathematics and the algorithms implemented in Python. Also, technology becomes outdated — fast. The code used in this tutorial will likely be meaningless in 5 years time. So for that reason, I’ve decided to also teach the mathematical side to Machine Learning that will not die out in a few years.
http://www.aparat.com/v/0scM5
Irene Chen A Beginner's Guide to Deep Learning.
What is #Deep_Learning ? It has recently exploded in popularity as a complex and incredibly powerful tool. This talk will present the basic concepts underlying deep learning in understandable pieces for complete beginners to #machine_learning.
http://www.aparat.com/v/Corus
Advanced users #Deep_Learning, anyone who has followed #machine_learning over the past years has heard it. In this talk I will go past the hype and show what deep learning actually means and how one goes about solving complex machine learning task with a minimum amount of code, with the help of theano, an amazing python library for deep learning.
https://www.python-course.eu/machine_learning.php
Tutorial and Online Course
#machine_learning
machine learning: robot jugglers
This is a completely new and incomplete chapter of our tutorial! We started work in January 2017!
#learn
https://github.com/4Catalyzer/pykubectl
A python bridge to kubectl providing additional functionalities useful for CD and #automation.
#machine_learning
https://www.kaggle.com/
The Home of #Data_Science & #Machine_Learning
Kaggle helps you learn, work, and play
https://www.udemy.com/machinelearning/learn/v4/content
#machine_learning A-Z™: Hands-On #Python & R In #Data_Science
https://django-ai.readthedocs.io/en/latest/readme.html
#Artificial_Intelligence for #Django
django-ai is a collection of apps for integrating statistical models into your Django project so you can implement #Machine_Learning conveniently.
https://machinelearningmastery.com/setup-python-environment-machine-learning-deep-learning-anaconda/
It can be difficult to install a #Python#machine_learning environment on some platforms.
Python itself must be installed first and then there are many packages to install, and it can be confusing for beginners.
In this tutorial, you will discover how to set up a Python machine learning development environment using #Anaconda.
https://see.stanford.edu/Course/CS229
This course provides a broad introduction to #machine_learning and #statistical_pattern_recognition.
Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control.
The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.
Students are expected to have the following background: