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