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 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

  1. IBM Machine Learning – https://console.ng.bluemix.net/catalog/services/ibm-watsonmachinelearning/
  2. Microsoft Azure Machine Learning – https://azure.microsoft.com/en-us/services/machinelearning/
  3. Google Machine Learning – https://cloud.google.com/products/machinelearning/
  4. Amazon Machine Learning – https://aws.amazon.com/machinelearning/
  5. Scikit-learn – https://github.com/scikit-learn/scikit-learn
  6. Shogun – https://github.com/shogun-toolbox/shogun
  7. Mahout – https://mahout.apache.org/
  8. Apache Spark MLlib – https://spark.apache.org/mllib/
  9. Weka – http://www.cs.waikato.ac.nz/ml/weka/
  10. Cloudera – http://www.cloudera.com/training/courses/intro-machine-learning.html
  11. BigML – https://bigml.com/
  12. TensorFlow – https://www.tensorflow.org/
  13. H20 – http://www.h2o.ai/
  14. 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!

Advertisements

Blockchain for Dummies!

Many companies are accepting bitcoins, many are not. Here is a list. These include Target, Tesla, Whole Foods, Microsoft, Home Depot, Intuit, Dell, PayPal/EBay, Sears, Bloomberg.com and many others.  With many companies accepting the change and others getting ready to, bitcoins are an extremely fast-spreading currency. The crypto-currencies have multiplied in the market place in recent years. QR codes are the biggest help in real-world bitcoin transfers. Using a smartphone and a Bitcoin wallet app, a user scans a label and presses a small buttoned aptly named “spend.”

Every transaction that happens between a buyer and seller or a transferor and transferee or between 2 members on the network, is verified and validated by “miners” to ensure it is secured and there is no risk of double spending. These miners are similar to VISA or MasterCard or Amex of the credit card world that provides a platform to exchange, validate and authorize. The miner creates a block of records which holds a copied record of all the verified transactions that have occurred in the network over the past ‘n’ minutes. Each transaction in every block is made at specific time and linked to previous block of transactions. Digital records are lumped together into “blocks” then bound together cryptographically and chronologically into a “chain” using complex mathematical algorithms. This encryption process, known as “hashing” is carried out by lots of different computers. If they all agree on the answer, each block receives a unique digital signature. The groups/chains of these blocks of transactions is referred to as Blockchain. The Blockchain is seen as the main technological innovation of Bitcoin, since it stands as proof of all the transactions on the network. Blockchain, or distributed ledger, technology is more secure, transparent, faster and less expensive than current financial systems. The distributed nature of a Blockchain database means that it’s harder for hackers to attack it – they would have to get access to every copy of the database simultaneously to be successful. It also keeps data secure and private because the hash cannot be converted back into the original data – it’s a one-way process.

In short, Blockchain is a method of recording data – a digital ledger of transactions, agreements, contracts – anything that needs to be independently recorded and verified as having happened. The big difference is that this ledger isn’t stored in one place, it’s distributed across several, hundreds or even thousands of computers around the world. In 2015, some of the leading financial institutions such as Visa, Goldman Sachs, Citi and other Wall Street incumbents joined venture capital firms to pour $488 million into the industry. In a World Economic Forum report released in September, “Deep Shift: Technology Tipping Points and Societal Impacts,” 58% of survey respondents said that they expected that by the year 2025, 10% of global gross domestic product will be stored on Blockchain technology. If banks started sharing data using a tailor-made version of Blockchain it could remove the need for middlemen, a lot of manual processing, and speed up transactions. If banks and other financial institutions are able to speed up transactions and take costs out of the system, it should mean cheaper, more efficient services for us.