...
Full Bio
Today's Technology-Data Science
256 days ago
How to build effective machine learning models?
256 days ago
Why Robotic Process Automation Is Good For Your Business?
256 days ago
IoT-Advantages, Disadvantages, and Future
257 days ago
Look Artificial Intelligence from a career perspective
257 days ago
Every Programmer should strive for reading these 5 books
578361 views
Why you should not become a Programmer or not learn Programming Language?
236811 views
See the Salaries if you are willing to get a Job in Programming Languages without a degree?
151779 views
Highest Paid Programming Languages With Highest Market Demand
136626 views
Have a look of some Top Programming Languages used in PubG
131916 views
Best Tools to develop Machine Learning Projects
- Provides ML model building, training, predictive modeling, and deep learning.
- The two services namely training and prediction can be used jointly or independently.
- This software is used by the enterprises, i.e., detecting clouds in a satellite image, responding faster to customer emails.
- It can be used to train a complex model.
- Amazon Machine Learning provides visualization tools and wizards.
- Supports three types of models, i.e., binary classification, multi-class classification, and regression.
- Permits users to create a data source object from the MySQL database.
- Used for developing production-grade computer vision, computer audition, signal processing, and statistics applications.
- Consists of more than 40 parametric and non-parametric estimation of statistical distributions.
- Contains more than 35 hypothesis tests including one way and two-way ANOVA tests, non-parametric tests like Kolmogorov-Smirnov test and many more.
- It has more than 38 kernel functions.
- An extensible framework for building scalable algorithms.
- Implementing machine learning techniques including clustering, recommendation, and classification.
- It includes matrix and vector libraries.
- Run on the top of Apache Hadoop using the MapReduce paradigm.
- This tool is designed for large scale learning.
- Mainly, it focuses on kernel machines like support vector machines for classification and regression problem.
- Allows linking to other machine learning libraries like LibSVM, LibLinear, SVMLight, LibOCAS, etc.
- It provides interfaces for Python, Lua, Octave, Java, C#, Ruby, MatLab, and R.
- It can process a vast amount of data like 10 million samples.