IBM, Google, Microsoft, Amazon and several companies are offering Machine Learning tools and API’s as part of their cloud offering. Last year, Amazon announced open-sourcing its deep learning library, Deep Scalable Sparse Tensor Network Engine, (DSSTNE), which is now available on GitHub. Followed by Google, it opened its SyntaxNet neural network framework for developers to build applications that can process human language. In Oct 2016, IBM renamed its Predictive Analytics Services as ‘Watson Machine Learning’. The focus is to provide deeper and more sophisticated self-learning capabilities as well as enhanced model management and deployment functionality within the service.
Machine learning has taken a main stage in many advanced analytical and predictive models. If you are user of Amazon.com or Netflix, you are consuming Machine learning recommendations. Similarly most of the websites customize and personalize to your taste, behavior and style based on machine learning that learns previous patterns and enables cognitive system to learn, reason and engaged with you in natural and personalized way.
Machine Learning has entered into every industry from Retail, Manufacturing, Banking, Cable, Games and sports and Media/Entertainment to many more. Today’s machine learning uses analytic models and algorithms that iteratively learn from data, thus allowing computers to find hidden insights without being explicitly programmed where to look. This means data analysts and scientists can teach computers to solve problems without having to recode rules each time a new data set is presented. Using algorithms that learn by looking at hundreds or thousands of data samples, computers can make predictions based on these learned experiences to solve the same problem in new situations. And they’re doing it with a level of accuracy that is beginning to mimic human intelligence.
Here are some of the top Machine learning tools
- IBM Machine Learning – https://console.ng.bluemix.net/catalog/services/ibm-watson–machine–learning/
- Microsoft Azure Machine Learning – https://azure.microsoft.com/en-us/services/machine–learning/
- Google Machine Learning – https://cloud.google.com/products/machine–learning/
- Amazon Machine Learning – https://aws.amazon.com/machine–learning/
- Scikit-learn – https://github.com/scikit-learn/scikit-learn
- Shogun – https://github.com/shogun-toolbox/shogun
- Mahout – https://mahout.apache.org/
- Apache Spark MLlib – https://spark.apache.org/mllib/
- Weka – http://www.cs.waikato.ac.nz/ml/weka/
- Cloudera – http://www.cloudera.com/training/courses/intro-machine-learning.html
- BigML – https://bigml.com/
- TensorFlow – https://www.tensorflow.org/
- H20 – http://www.h2o.ai/
- Veles – https://velesnet.ml/
Here’s the last seen Machine Intelligence Landscape published in early 2016 by Shivonzillis.com
Tell us about your ML experience with any of the tools above and any new tools that you want to share with other readers!