Machine Learning Tools And Frameworks

Here we will learn about some useful machine learning tools and frameworks which is listed below:


  • Tableau

  • Weka

  • SAS studio

  • Rapid Miner

  • Azure Machine Studio

  • Azure Cognitive services

  • DataBricks (Big Data Spark)

  • BigML

  • Heroku

  • Kafka

  • cassandra


1. Tableau


Tableau is a data analytics and visualization tool which is used currently in most of data visualization industries or software industries to visualization of given data. It help to understanding data easily by any non technical person. Many businesses or industries need to visualize our data so it covers huge areas. It use drag and drop features so any non-technical person can easily understand its work. This feature helps to perform tasks like sorting, comparing and analyzing, very easily and fast. Tableau is also support multiple sources, including Excel, SQL Server, and cloud-based data repositories which makes it an excellent choice for Data Scientists.

Why we will use tableau

There are many terms by which we can easily say that "why we will use tableau"

  • Fast Analytics

  • Ease of Use

  • Big Data, Any Data

  • Smart Dashboards

  • Update Automatically

  • Share in Seconds


Product Suite

Tableau support many product suite:

  • Tableau Desktop

  • Tableau Public

  • Tableau Online

  • Tableau Server

  • Tableau Reader

Installation of Tableau

You can download tableau directly from official website, which is any try(for 30 days) or paid using below link:

https://www.tableau.com/products/desktop/download

2. Weka

Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code.

It contains tools for:

  • data pre-processing,

  • classification,

  • regression,

  • clustering,

  • association

  • rules, and

  • visualization.

It is also well-suited for developing new machine learning schemes.


How it will Download

You can download(Latest version 3.6) it using below link:

https://www.cs.waikato.ac.nz/ml/weka/


3. SAS studio

When you use SAS University Edition, you are using SAS Studio to access SAS. Many people program in SAS by using an application on their PC desktop or SAS server. It is a tool that you can use to write and run SAS code through your web browser.

By using SAS you can access your data files, libraries, and existing programs and write new programs. When you use SAS Studio, you are also using SAS software behind the scenes. SAS Studio connects to a SAS server in order to process SAS commands.


With SAS software, you can complete these tasks:

  • access any format data: including SAS tables, Microsoft Excel tables, and database files.

  • manage and manipulate your existing data to get the data that you need.

  • The reports that you create can be saved in a wide variety of formats, including HTML, PDF, and RTF.

How we can install it

Download university edition using below link:

https://www.sas.com/en_in/software/university-edition/download-software.html

4. Rapid Miner


Data preparation is an important part of data science, their are many techniques are used for this but it will take lots of time to prepare data.


It is a brand-new data prep tool which used to help speed productivity so that it prepare data within short of time(or help to time-consuming data preparation tasks).


How we can install it

use below link to download this:

https://docs.rapidminer.com/latest/server/install/


5. Azure Machine Studio

Azure Machine Learning Studio is a the browser-based workbench for Machine Learning. In this you can create free account and get 10GB space, after go apart from 10GB you can get it using paid.


you can look all the details regarding limitations using below link:


https://azure.microsoft.com/en-us/pricing/details/machine-learning-studio/

After an account is created, you can log in to:


https://studio.azureml.net/

It is collection of complete tutorials and guide related to Azure.


Azure Machine Learning can be used for different types of machine learning,

  • classical ml

  • deep learning,

  • supervised, and

  • unsupervised learning.

By using this you can write Python or R code with the SDK or work with no-code/low-code options in the studio, you can build, train, and track machine learning and deep-learning models in an Azure Machine Learning Workspace.









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