Do you want to learn about machine learning? Open the link above to get to know the basics of machine learning, its types, and examples with flowcharts.
Get an estimate of Machine learning and Data Science Consulting rates in the United States. We look at freelance platforms such as UpWork as well as agencies.
This is the website for Data Science at the Command Line, published by O’Reilly October 2014 First Edition. This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You’ll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data.
The codebase contains a replica of the AlphaZero methodology, built in Python and Keras. Gain a deeper understanding of how AlphaZero works and adapt the code to plug in new games.
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How can I use continuous sequence values with... Learn more about hmmestimate, hidden, markov, model, hmm, continuous, emission, emit, state, states Statistics and Machine Learning Toolbox
In this post, I want to show how I use NLTK for preprocessing and tokenization, but then apply machine learning techniques (e.g. building a linear SVM using stochastic gradient descent) using Scikit-Learn.
What are Convolutional Neural Networks and why are they important? Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. ConvNets have been successful in identifying faces, objects and traffic signs apart from powering vision in robots and self driving cars. Figure 1:…
In the previous post on Support Vector Machines (SVM), we looked at the mathematical details of the algorithm. In this post, I will be discussing the practical implementations of SVM for classification as well as regression. I will be using the iris dataset as an example for the classification problem, and a randomly generated data as an example for the regression problem.
J. Zhang, Y. Dong, Y. Wang, J. Tang, and M. Ding. Proceedings of the 28th International Joint Conference on Artificial Intelligence, page 4278–4284. AAAI Press, (Aug 10, 2019)