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IBM Data Science Experience (DSX) platform (now known as Watson Studio)

Learn how to develop in Python,R and Spark

RafieTarabay

Tags: Big Data and analytics, Cloud computing

Published on January 17, 2018 / Updated on November 9, 2020

Overview

Skill Level: Beginner

What is machine learning? How to develop machine learning using Python? How to develop machine learning using Spark? How to develop machine learning using R? How to use IBM Data Science Experience (DSX) platform to run your script?

Ingredients

development background to write some simple scripts for data analysis in Python, R, and Spark

Step-by-step

1. IBM Watson

Watson an IBM brand for the next generation of cognitive computing solutions.
Watson are trained using machine learning/Deep learning algorithms to sense, predict, infer and, in some ways, think.

IBM Watson Data Science Experience : advanced analytics capabilities for data scientists (write code for ML/Deep learning)

IBM Watson Analytics: removes the complexity and delivers a user friendly tools for business professional

IBM Watson Services on BlueMix for Application Developer

  • Watson Conversation service (used to develop Help Desk Assistant chatbot)
  • Watson Visual Recognition service
  • Natural Language Classifier service (used in Healthcare questions and answers)
  • Watson Language Translator service
  • Watson Speech to Text and Text to Speech services
  • Watson Natural Language Understanding service (Sentiment and personality analysis)

Note
IBM Watson Explorer: an application designed for a local install, not a cloud base solution, old name was IBM Omnifind

2. IBM Data Science Experience (DSX) platform

IBM DSX is a powerful computational engine based on Apache Spark Executors.

It has a strong computing capacity in the back end.

It currently supports Python, R, and Scala.

Using IBM DSX, you can create a Python, R, or Scala, notebook-based project and create a data connection to your data source.

You have options to load all types of Machine Learning algorithms that are supported by runtime from KNN and RandomForest to TensorFlow.

You can use notebook to :
Loaded your data
Created data sets
Modeled, trained, and validated your data

How to use platform for free?

Register for IBM Cloud here : http://ibm.biz/MLChallenge
Log in to IBM DataScience Experience with your IBM Cloud credientials - https://datascience.ibm.com

3. Use IBM Data Science Experience for Machine Learning

Download the attached PPT https://www.ibm.com/developerworks/community/files/app#/file/1c0ac155-72c1-439d-be3c-7dd645a0243f

to learn

  1. How to open free account on IBM Data Science Experience (DSX) platform and how to use it

  2. What is the most common machine learning algorithms

  3. How to write a simple python script for machine learning

  4. How to use NLTK, and textblob for Natural Language Processing and Sentimental Analysis

  5. How to use OpenCV for Computer Vision

  6. How to develop deep learning using Neural Network in Keras

  7. How to develop machine learning using Spark

  8. How to make Basket market analysis using R Studio