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Top 10 Machine Learning Algorithms for Beginners

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The study of ML algorithms has gained immense traction post the Harvard Business Review articleterming a'Data Scientist' as the'Sexiest job of the 21st century'. So, for those starting out in the field of ML, we decided to do a reboot of our immensely popular Gold blog The 10 Algorithms Machine Learning Engineers need to know - albeit this post is targetted towards beginners. ML algorithms are those that can learn from data and improve from experience, without human intervention. Learning tasks may include learning the function that maps the input to the output, learning the hidden structure in unlabeled data; or'instance-based learning', where a class label is produced for a new instance by comparing the new instance (row) to instances from the training data, which were stored in memory. 'Instance-based learning' does not create an abstraction from specific instances. Supervised learning can be explained as follows: use labeled training data to learn the mapping function from the input variables (X) to the output variable (Y).


Build a simple Neural Network for Breast Cancer Detection using Tensorflow.js

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There's more and more research done on detecting all types of cancers in early stages and thus increasing probability of survival. Since I've been passionate about machine learning for a while, I decided to bring my own contribution to this research and learn to train my own neural network detection model. The twist was to build it using Tensorflow with JavaScript, not with Python. We're also using React to manage the state and display the data we get back from the model. For this tutorial, I chose to work with a breast cancer dataset.


Top 10 Machine Learning Algorithms for Beginners

#artificialintelligence

The study of ML algorithms has gained immense traction post the Harvard Business Review article terming a'Data Scientist' as the'Sexiest job of the 21st century'. So, for those starting out in the field of ML, we decided to do a reboot of our immensely popular Gold blog The 10 Algorithms Machine Learning Engineers need to know -- albeit this post is targeted towards beginners. ML algorithms are those that can learn from data and improve from experience, without human intervention. Learning tasks may include learning the function that maps the input to the output, learning the hidden structure in unlabeled data; or'instance-based learning', where a class label is produced for a new instance by comparing the new instance (row) to instances from the training data, which were stored in memory. 'Instance-based learning' does not create an abstraction from specific instances. Supervised learning can be explained as follows: use labeled training data to learn the mapping function from the input variables (X) to the output variable (Y).



How to Build a Machine Learning Model

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How to Build a Machine Learning Model A Visual Guide to Learning Data Science Jul 25 · 13 min read Learning data science may seem intimidating but it doesn't have to be that way. Let's make learning data science fun and easy. So the challenge is how do we exactly make learning data science both fun and easy? Cartoons are fun and since "a picture is worth a thousand words", so why not make a cartoon about data science? With that goal in mind, I've set out to doodle on my iPad the elements that are required for building a machine learning model.