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Pragmatic Machine Learning Toolkit @ AWS Platform


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Machine Learning library Path And Package’s

Machine Learning can unlock valuable insights from your organization’s data and provide very interesting predictive Analytics to help make informed decisions.

Machine Learning is a machine service that allows you to easily build predictive applications, including fraud detection, demand forecasting, and click prediction For more information. Machine Learning uses powerful algorithms that can help you create machine learning models by finding patterns in existing data, and using these patterns to make predictions from new data as it becomes available. Basically, machine learning is the subset of Artificial Intelligence that allows a program to take in some data set and extract patterns from it. We do this by using the typical ML pipeline: training data => features => trained classifier.Where machine learning – at its core – is about the use and development of these learning algorithms, data science is more about the extraction of knowledge from data to answer particular question or solve particular problems.

Pragmatic Machine Learning Toolkit allows any Data Scientist to start using some of the very popular libraries of Machine Learning in in the Amazon cloud. A big advantage of such a AMI is that you scale up or scale down the size of the instance to match the performance needed.

1] What can I do with Pragmatic Machine & Deep Learning Toolkit?
  • You can use Pragmatic Machine & Deep Learning Toolkit to create a wide variety of predictive applications. For example, you can use Pragmatic Machine & Deep Learning Toolkit to help you build applications that flag suspicious transactions, detect fraudulent orders, forecast demand, personalize content, predict user activity, filter reviews, listen to social media, analyze free text, and recommend items.
2] What is advantage of Pragmatic Machine & Deep Learning Toolkit?
  • Pragmatic has public Machine & Deep Learning Toolkit AMI on AWS marketplace. This AMI has installed all machine learning Libraries (Pandas, Scikit-learn, NLTK, Theano, CAFFE, TensorFlow, TORCH, Spark, Gensim, Elastics, CNTK) . Customer can take advantage, and one single click launch server on AWS cloud.
3] What is support from pragmatic ?
  • Pragmatic has launched a Email Helpdesk support for Machine & Deep Learning Toolkit . It is meant to solve practical issues while configuring and using Machine Learning Libraries. We have ML consultants who can understand your business problem and recommend Machine Learning Algorithms to solve your business problems. We have a team of highly specialized ML experts along with Python and Js programmers to solve any kind of Predictive Analytics problem at hand.
4] What is the service availability of Pragmatic Machine Learning Toolkit?
  • Pragmatic Machine & Deep Learning is designed for high availability. There are no maintenance windows or scheduled downtimes. The API for model training, evaluation, and batch prediction runs in Amazon’s proven, high-availability data centers, with servicestack replication configured across three facilities in each AWS region to provide fault tolerance in the event of a server failure or Availability Zone outage.
5] Is any charge for the Pragmatic Machine & Deep learning AMI launch on AWS cloud.
  • No
6] What should instance type on AWS cloud for Pragmatic machine & Deep learning libraries.
  • t2-medium AND storage 50 GB
7] Pragtech machine & Deep learning Toolkit features.
  • Operating System:- Ubuntu 16.04 lts
    Installed Library’s :- Pandas, Scikit-learn, NLTK, Theano, CAFFE, TensorFlow, TORCH,
    Spark, Gensim, Elastics, CNTK
    Python:- python2/3
8] What security measures does Pragmatic Machine & Deep Learning Toolkit it have?
  • Pragmatic Machine & Deep Learning Toolkit ensures that ML models and other system artifacts are encrypted in transit and at rest. Requests to the Pragmatic Machine & Deep Learning Toolkit API and console are made over a secure (SSL) connection. You can use AWS Identity and Access Management (AWS IAM) to control which IAM users have access to specific Pragmatic Machine & Deep Learning Toolkit actions and resources.
8] What are data science and Pragmatic Machine & Deep Learning Toolkit?
  • In short, machine learning algorithms are algorithms that learn (often predictive) models from data. I.e., instead of formulating “rules” manually, a machine learning algorithm will learn the model.

    Data science is an emerging discipline that combines techniques of computer science, statistics, mathematics, and other computational and quantitative disciplines to analyze large amounts of data for better decision making.

    Machine learning is often a big part of a “data science” project, e.g., it is often heavily used for exploratory analysis and discovery (clustering algorithms) and building predictive models (supervised learning algorithms). However, in data science, you often also worry about the collection, wrangling, and cleaning of your data (i.e., data engineering), and eventually, you want to draw conclusions from your data that help you solve a particular problem. The typical skills of a data scientists are

    • Computer science: programming, hardware understanding, etc.
    • Math: Linear algebra, calculus, statistics
    • Communication: visualization and presentation
    • Domain knowledge



Johnny_five library path
Library name:- node_modules
ubuntu@ip-x-x-x-x:~$ cd /opt/

Now open your text editor and create a new file called "johnny_five_test.js", in that file type or paste the following:
var five = require("johnny-five"),
board = new five.Board();

board.on("ready", function() {
// Create an Led on pin 13
var led = new five.Led(13);

// Strobe the pin on/off, defaults to 100ms phases

Make sure the board is plugged into your host machine (desktop, laptop, raspberry pi, etc). Now, in your terminal, type or paste the following:
Should run below command through root privilege or sudo users
ubuntu@ip-x-x-x-x:~$ sudo node johnny_five_test.js

After run the above js script. Success should look like this


#sudo pip install xlrd
#sudo pip install xlwt
#sudo pip install openpyxl
#sudo pip install XlsxWriter



Testing requires having the nose library. After installation, the package can be tested by executing from outside the source directory:
nosetests -v sklearn




python `python -c "import os, theano; print(os.path.dirname(theano.__file__))"`/misc/check_blas.py
run the Theano/BLAS speed test:






Generating OpenBLASConfigVersion.cmake in /opt/OpenBLAS/lib/cmake/openblas
Install OK!









To activate the CNTK environment, run
source "/home/ubuntu/cntk/activate-cntk"

Please checkout tutorials and examples here:


Johnny-five is a JavaScript Robotics programming framework. It enables you to read and write to and from your Arduino board using JavaScript. It’s open source and has a very straightforward API that resembles jQuery, so it’s actually very likely that it will already look very familiar to you.

Johnny-Five javascript robotics library comes in really handy. It comes with a huge library of pre-built components that work with almost every kind of sensor, servo, LED, button, board out there. Getting the Johnny-Five node to work in Raspbian has been a bit tricky.


The Pandas Python library is built for fast data analysis and manipulation.

The first step in our exploration is to read in the data and print some quick summary statistics. In order to do this, we’ll us the Pandas library. Pandas provides data structures and data analysis tools that make manipulating data in Python much quicker and more effective. The most common data structure is called a dataframe. A dataframe is an extension of a matrix, so we’ll talk about what a matrix is before coming back to dataframes.



CNTK stood for “Computational Network Toolkit”. Weirdly, the name was changed to the “Microsoft Cognitive Toolkit” but the acronym is still CNTK.This is a command line program that can do regular and deep neural network analyses. Because CNTK was originally developed as an internal Microsoft tool, and because it’s under very rapid development, the existing documentation is pretty weak because it’s incomplete and lags behind the code base.

CNTK is a direct competitor to Google’s TensorFlow tool. I’ve used both TensorFlow and CNTK, and I prefer CNTK. Both tools have a ton of room for improvement, primarily in the area of documentation. But CNTK just has a bit of a nicer feel to me. Of course this is partially due to the fact that CNTK is mostly a Windows tool and TensorFlow runs on Ubuntu, and I work much more often on Window than on Linux.


Scikit-learn, a popular and user-friendly Python package for machine learning. This library provides a range of supervised and unsupervised learning algorithms via a consistent interface in Python. It is licensed under a permissive simplified BSD license and is distributed under many Linux distributions, encouraging academic and commercial use.

Extensions or modules for SciPy care conventionally named SciKits. As such, the module provides learning algorithms and is named scikit-learn.



NLTK, the Natural Language Toolkit, is a platform for building Python programs to work with human language data. It provides interfaces to more than 50 corpora and lexical resources such as WordNet, along with wrappers for natural language processing languages, and a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning.

NLTK originated at the University of Pennsylvania, and it is currently being used in courses at 32 universities worldwide. Highlights of NLTK include lexical analysis (that is, word and text tokenization); n-gram and collocations; part-of-speech tagging; a tree model and text chunker; and named-entity recognition.

NLTK is available for Windows, OS X, and Linux. There is an online book about NLTK, Natural Language Processing with Python. NLTK requires Python 2.7 or 3.2 or later.


If there is a “magic sauce” at Google today, it is machine learning and deep neural networks. The machine learning package Google uses is TensorFlow, assisted by Tensor processing units (TPUs) in its datacenters. TensorFlow was developed by the Google Brain team over several years and released to open source in November 2015.

TensorFlow does computation using data flow graphs for scalable machine learning. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code.


Theano is a Python library that lets you define, optimize, and evaluate mathematical expressions, especially ones with multidimensional arrays. It was developed at the LISA lab of the University of Montreal to support rapid development of efficient machine learning algorithms, and it has been used to support large-scale, computationally intensive scientific investigations since 2007. The University of Montreal uses Theano in its machine learning and deep learning classes.


Caffe is a deep learning framework from the Berkeley Vision and Learning Center, released under the BSD 2-Clause license. The core Caffe framework is written in C++ with support for CUDA on Nvidia GPUs and the ability to switch between running on CPUs and GPUs. Caffe has command-line, Python (including Jupyter Notebook), and Matlab interfaces.


Torch computing framework with wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient, thanks to the LuaJIT scripting language and an underlying C/CUDA implementation. (There is also an OpenCL port.) Torch comes with a large ecosystem of community-created packages in machine learning, computer vision, signal processing, parallel processing, image and video processing, and networking among others, and it builds on top of the Lua community.


Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming.


Gensim is a free Python library designed to automatically extract semantic topics from documents, as efficiently (computer-wise) and painlessly (human-wise) as possible.

Gensim is designed to process raw, unstructured digital texts (“plain text”). The algorithms in gensim, such as Latent Semantic Analysis, Latent Dirichlet Allocation and Random Projections discover semantic structure of documents by examining statistical co-occurrence patterns of the words.


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