Goto

Collaborating Authors

Top 10 IPython Notebook Tutorials for Data Science and Machine Learning

@machinelearnbot

This is a great project undertaken by Jordi Warmenhoven to implement the concepts from the book An Introduction to Statistical Learning with Applications in R by James, Witten, Hastie, Tibshirani (2013) in Python (the book has practical exercises in R, as you may have guessed). The book is freely available in as a PDF, which makes this repo even more attractive to those looking to learn.


Top 10 IPython Notebook Tutorials for Data Science and Machine Learning

#artificialintelligence

This post is made up of a collection of 10 Github repositories consisting in part, or in whole, of IPython (Jupyter) Notebooks, focused on transferring data science and machine learning concepts. This warmup notebook is from postdoctoral researcher Randal Olson, who uses the common Python ecosystem data analysis/machine learning/data science stack to work with the Iris dataset. Aaron Masino has shared a series of very detailed, very technical machine learning IPython Notebook learning resources. From UC Boulder's Research Computing group, this older collection of notebooks (it's from way back in Fall 2013) covers a wide range of material, with an apparent focus on Linux command line-powered data management.


Top 10 IPython Notebook Tutorials for Data Science and Machine Learning

#artificialintelligence

This is a great project undertaken by Jordi Warmenhoven to implement the concepts from the book An Introduction to Statistical Learning with Applications in R by James, Witten, Hastie, Tibshirani (2013) in Python (the book has practical exercises in R, as you may have guessed). The book is freely available in as a PDF, which makes this repo even more attractive to those looking to learn.


Free Online Resources To Get Hands-On Deep Learning

#artificialintelligence

With deep learning gaining its momentum in fields like self-driving cars, object detection, voice assistants and text generation, to name a few, the demand for deep learning experts in organisations has also significantly increased. As a matter of fact, big tech companies like Facebook, Google, Apple as well as Microsoft have started investing heavily on deep learning projects which, in turn, increase the number of deep learning open jobs in the market. Having said that, deep learning is one of the complex subsets of machine learning and envelops several layers of components which cannot be grasped in a day. Hence, despite the high demand, there is indeed a gap in deep learning talent for organisations. Not only does it come with prerequisites of linear algebra and calculus knowledge but also enough interest to pursue a complicated subject like deep learning.


Getting started with Python Machine Learning

#artificialintelligence

Everyone and their mother are learning about machine learning models, classification, neural networks, and Andrew Ng. However, there are wrappers that ease the pain and make working with Theano simple, such as Keras, Blocks and Lasagne. Keras is a fantastic library that provides a high-level API for neural networks and is capable of running on top of either Theano or TensorFlow. Another popular deep learning framework is Torch, which is written in Lua.