Top 14 Machine Learning Tools for Data Scientists

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 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

  1. IBM Machine Learning –
  2. Microsoft Azure Machine Learning –
  3. Google Machine Learning –
  4. Amazon Machine Learning –
  5. Scikit-learn –
  6. Shogun –
  7. Mahout –
  8. Apache Spark MLlib –
  9. Weka –
  10. Cloudera –
  11. BigML –
  12. TensorFlow –
  13. H20 –
  14. Veles –

Here’s the last seen Machine Intelligence Landscape published in early 2016 by

Tell us about your ML experience with any of the tools above and any new tools that you want to share with other readers!


AI to the rescue of C-Suite

Artificial Intelligence (AI) has been in discussion for past several decades as science fiction and experiments in research and labs. However, in past 2-3 years, AI has come in the forefront of discussion. It has started gaining adaption in C-Suite and business world. Many industries – Healthcare, Retail, Banking, Consumer Products, Insurance, Manufacturing, Auto among several others are now experimenting with AI tools and technologies.

A 2015 Tech Pro Research survey indicated that 24 percent of businesses across industries are currently using AI or had plans to do so within the year. There are three forms of AI – Assisted, Augmented and Autonomous. You can see these scenarios in auto industry that is taking cars using Assisted AI to Augmented AI with a goal to have Autonomous AI driven cars in next few months or a year.

Now, how do we apply the AI in business strategy and decision making process. Despite several advancements in Cognitive technologies in past year, having a autonomous AI for business strategy is far from reality. However, one of the leading start up management consulting firm 7F Consulting is claiming to have a solution framework to include all market news, industry regulations, economic indicators, social and competitive intelligence to become a AI Assisted strategy advisor. Imagine a solution like this that is integrated with Alexa or Siri and it can run hypothesis with decision tree for you and using the data, come back with probabilistic decision for you to consider. Over course of multiple interactions, the system learns what insights you need, what actions you take and what drives results using Management framework and delivers tailored results.

The potential of Artificial Intelligence for organizations is enormous and if the projections turn out to be true and in the coming years the AI market will grow to a multiple billion dollar market, doing business can take up a whole new meaning. Resulting in fewer employees required while significantly improving your bottom line results. We are all aware of how jobs that once were the exclusive domain of humans such as facial recognition, sarcastic comment analysis, automobile operation, and language translation are now being done with software. If your organization is not already doing so, you should encourage it to undertake pilot projects involving AI to gain experience and better understand its capabilities and, perhaps more important, its limitations.