Python is a leading development platform for data scientists working on machine learning projects. The tutorial presentation below offers an introduction to the scikit-learn package and to the central ...
Over the past year I’ve reviewed half a dozen open source machine learning and/or deep learning frameworks: Caffe, Microsoft Cognitive Toolkit (aka CNTK 2), MXNet, Scikit-learn, Spark MLlib, and ...
Deep Learning with Yacine on MSN
Visualizing high-dimensional data using PCA in Scikit-Learn
Simplify complex datasets using Principal Component Analysis (PCA) in Python. Great for dimensionality reduction and ...
TensorFlow, Spark MLlib, Scikit-learn, PyTorch, MXNet, and Keras shine for building and training machine learning and deep learning models. If you’re starting a new machine learning or deep learning ...
Late last year, my colleagues on the Social Science team were working on a new survey weighting scheme that would greatly improve the precision of our public opinion data. To make it work, they needed ...
Machine learning with neural networks is sometimes said to be part art and part science. Dr. James McCaffrey of Microsoft Research teaches both with a full-code, step-by-step tutorial. A binary ...
A regression problem is one where the goal is to predict a single numeric value. For example, you might want to predict the price of a house based on its square footage, age, number of bedrooms and ...
Spam filtering, face recognition, recommendation engines — when you have a large data set on which you’d like to perform predictive analysis or pattern recognition, machine learning is the way to go.
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