Learn the best machine learning tools to efficiently build ML models
With ever-growing data generation and its usage, the demand for machine learning models is multiplying. As ML systems encompass algorithms and rich ML libraries, it helps analyze data and make decisions. There is no wonder that machine learning is gaining more visibility as ML applications are dominating almost every aspect of the modern-day world. With rapidly increasing exploration and adoption of this technology in businesses, it is setting the ground for ample employment opportunities. However, landing a career in this disruptive field, you must be well-equipped and familiar with some of the best machine learning tools to create efficient and functional ML algorithms.
Here are the 10 best machine learning tools to look for in 2021.
1. TensorFlow
TensorFlow is a free machine learning tool that can be used on platforms such as Linux, Mac OS, and Windows. It offers a JavaScript library that helps in machine learning. It helps users to build and train their models. TensorFlow is an excellent ML tool for deep learning systems and neural nets. Users can also run their existing models using TensorFlow.js, a model converter.
2. KNIME
KNIME has been utilized in pharmaceutical research and other areas like CRM customer data analysis, business intelligence, text mining and financial data analysis. It is a free, open-source data analytics, reporting and integration platform. This machine learning tool can integrate the code of programming languages like R, Python, Java, JavaScript, C, C++, etc. KNIME is useful for beginners as this platform is built for powerful analytics on a GUI-based workflow. This means, if users don’t have the knowledge of how to code, they will be able to work using KNIME and derive insights.
3. Scikit-Learn
Scikit-Learn is a free machine learning library for Python. It helps in data mining and data analysis, and provides models and algorithms for classification, regression, clustering, dimensional reduction, model selection, and pre-processing. Built on NumPy, SciPy, and Matplotlib, Scikit-Learn involves an array of efficient tools for machine learning and statistical modeling.
4. PyTorch
PyTorch is based on the Torch library used for applications such as computer vision and natural language processing, primarily developed by Facebook’s AI Research lab. This open-source machine learning library helps in building neural networks through Autograd Module. It provides a variety of optimization algorithms for building neural nets. PyTorch can be used on cloud platforms and can provide distributed training, with numerous tools and libraries.
5. BigML
It is a comprehensive machine learning platform that offers a host of ML algorithms to solve complex, real-world problems through a unified and integrated framework. BigML consists of a wide array of useful machine learning features, such as classification, regression, cluster analysis, time-series forecasting, anomaly detection, topic modeling, etc. As it comes with an extensive range of features well-integrated within a convenient Web UI, it allows users to load their dataset, build and share their machine learning models, train and assess their models, and make new predictions.
6. Weka
Weka is a data mining, open-source machine learning software. It can be accessed through a graphical user interface (GUI), standard terminal applications, or a Java API. Weka encompasses a collection of visualization tools and algorithms for data analysis and predictive modeling and is widely used for teaching, research, and industrial applications. It supports various standard data mining tasks, particularly, data pre-processing, clustering, classification, regression, visualization and feature selection.
7. Colab
Colab is a Google Research product for machine learning tasks. It allows developers to write and execute Python code through their browser. Colab notebooks let users combine executable code and rich text in a single document, along with images, HTML, LaTeX and more. When a user creates their Colab notebooks, they are stored in their Google Drive account that can be easily shared with their peers.
8. Amazon Machine Learning
Amazon offers a cohort of machine learning tools. Amazon Machine Learning (AML) is a cloud-based, robust machine learning software application that can be used by all levels of web and mobile app developers. AML provides wizards & visualization tools as well as supports three types of models: multi-class classification, binary classification, and regression.
9. IBM Watson Studio
IBM Watson Studio allows users to build, run and manage machine learning models. It offers all the tools required to solve business problems through a collaborative data experience. It brings together vital open-source tools, including RStudio, Spark and Python, in an integrated environment, along with additional tools such as a managed Spark service and data shaping facilities, in a secure and governed environment.
10. Apache Mahout
As an open-source, distributed linear algebra framework, Apache Mahout helps mathematicians, statisticians and data scientists to execute their algorithms. It is a project of the Apache Software Foundation to make free implementations of distributed or otherwise scalable ML algorithms focused primarily on linear algebra. It contains Java libraries for common maths operations.
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