Machine learning is one of the most revolutionary technologies to make lives easier. It is a subfield of artificial intelligence that analyzes data, builds models and makes predictions. Due to its popularity and rich applications, every technology enthusiast wants to learn and build new machine learning apps. But it's crucial to grasp machine learning technologies if you want to create ML models. Mastering machine learning tools allows you to play with data, train your models, discover new methods, and create algorithms.
Different tools, software and platforms are available for machine learning and new software and tools are developing day by day. Despite the many options and availability of machine learning tools, choosing the best tool for your model can be a challenging task. If you choose the right ML Companies for your business model, you can do it faster and more efficiently. In this topic, we will discuss some popular and commonly used machine learning tools and their features.
Top 5 Machine Learning Tools
# Scikit-learn
Python machine learning development makes use of Scikit-learn. It offers a Python programming language library and expands upon NumPy, pandas, SciPy, and Matplotlib.
Although the interface is Python, c-libraries are used for performance such as NumPy, LAPACK, LibSVM and careful use of Cython for arrays and matrix operations.
Features:
- It supports data mining and data analysis.
- It provides models for algorithms such as classification (K nearest neighbors), regression (linear and logistic regression), clustering (K-means and K-means++), etc.
- It also provides functions such as model selection and preprocessing.
- It provides easy-to-understand documentation.
- With scikit-learn, variables for any particular algorithm can be changed while calling objects.
# Pytorch
Based on the Torch library, PyTorch is an open source machine learning framework. This framework is free and open source and developed by FAIR (Facebook's AI Research Lab). It is one of the popular ML frameworks used for various applications including computer vision and natural language processing.
Read More: Importance of Natural Language Processing
Features:
- It allows developers to build neural networks using the AutoGrade module.
- It is more suitable for deep learning research with good speed and flexibility.
- It can also be used on cloud platforms.
- It includes tutorial courses, various tools and libraries.
- Additionally, it offers a dynamic computational graph, which increases the popularity of this library.
- As a result, there is no lag when the network behaviour changes at random.
- Because of the hybrid front end, it is simple to use.
- It is freely accessible.
# Amazon Machine Learning (AML)
Amazon offers a large number of machine learning tools and one of them is Amazon Machine Learning or AML. Amazon Machine Learning (AML) is a cloud-based and robust machine learning software application that is widely used to build machine learning models and make predictions. Additionally, it combines information from several sources, such as Redshift, Amazon S3, or RDS.
Features:
- AML provides visualization tools and wizards.
- Allows users to identify patterns, create mathematical models, and make predictions.
- It provides support for three types of models namely multi-class classification, binary classification and regression.
- It allows users to import a model or export a model from Amazon Machine Learning.
- It also covers the core concepts of machine learning, including ML models, data sources, evaluations, real-time predictions, and batch predictions.
Read More: Modernizing Food Processing with AI
# Apache Mahout
Apache Mahout is an open source project of the Apache Software Foundation used to develop machine learning applications focused primarily on linear algebra. It is a distributed linear algebra framework and a mathematically expressive Scala DSL that enables developers to immediately implement their own algorithms. It also provides Java/Scala libraries to perform mathematical operations based mainly on linear algebra and statistics.
Features:
- It allows developers to implement machine learning techniques including recommendation, clustering and classification.
- It is a productive framework for putting scalable algorithms into practice.
- It includes matrix and vector libraries.
- It supports multiple distributed backends (including Apache Spark)
- It runs on top of Apache Hadoop using the MapReduce model.
Read More: Practical Use of A.I. in Transportation
# Google ML Kit for Mobile
For mobile app developers, Google offers an ML kit packed with machine learning and technology expertise to build more robust, optimized and personalized apps. This tool kit can be used for face detection, text recognition, landmark detection, image labeling and barcode scanning applications. It can be used to operate offline as well.
Features:
- ML Kit is optimized for mobile.
- It takes advantage of various machine learning technologies.
- It provides simple APIs that enable powerful use cases in your mobile apps.
- It includes Vision API and Natural Language APIS to recognize faces, text and objects and provide different language recognition & reply suggestions.
Last words
Machine learning is an exciting topic. However, to get the best out of AI and ML you need to use the best machine learning tools and frameworks. Most of the applications of machine learning tools listed are free and open source, allowing users to customize them according to their individual needs.
