The world of machine learning is always expanding and changing. As such, there are many tools to aid you in your quest for knowledge.
Most likely, you already have some knowledge of machine learning and its potential to revolutionize industries. But when it comes down to building a successful project, there's no escaping hard work, expertise—and picking the right tools.
The size of the machine learning market has been rising steadily. The deep learning software category, expected to reach almost $1 billion by 2025, is the most significant subsegment of this market. According to recent machine learning market research, the demand for AI-enabled hardware and personal assistants is anticipated to grow rapidly.
The following list offers 10 of the best tools for machine learning projects. The selection is based on their usefulness and versatility in various contexts, including training models, deploying them at scale and analyzing data.
TensorFlow
Google Brain's engineers and researchers initially created an open-source machine learning framework called TensorFlow. The library was initially created for ML and deep neural network research.
Sklearn
One of Python's most well-liked and reliable tools for carrying out machine learning-related tasks is sklearn (also known as scikit-learn), first created by David Cournapeau in the 2007 Google Summer of Code (GSoC) program.
Shogun
Shogun is an open-source machine-learning framework built on C++. It offers a broad range of complete machine-learning algorithms that are both efficient and optimized. Support vector machines are among the kernel machines in Shogun that are used to address regression and classification problems.
Colaboratory
Google Colab, also known as Colaboratory by Google, is a free cloud computing platform for data science and machine learning. It eliminates any physical restrictions that might exist when using machine learning models. Run complex models and algorithms.
Weka
Weka (Waikato Environment for Knowledge Analysis) is an open-source toolkit that can be used to create machine learning models and use them in practical data mining scenarios. It is available under the GNU GPL (General Public License) and includes tools for data preprocessing, the implementation of numerous ML algorithms, and visualization.
IBM Cloud
More than 170 products and cloud computing tools comprise the entire IBM cloud services stack for business-to-business (B2B) organizations. Like many other all-encompassing cloud computing services like AWS, Microsoft Azure, and Google Cloud, IBM Cloud includes all three of the primary service models (or varieties) of cloud computing.
Google ML kit for Mobile
Google offers the ML Kit to mobile app developers with machine learning know-how and technology to build more reliable, optimized, customized apps. This toolkit can also be used for barcode scanning, landmark detection, face detection, and text recognition applications. It can also be used for offline work.
Apache Mahout
The Apache Software Foundation's open-source project Apache Mahout is used to creating machine learning programs primarily focusing on linear algebra. With its distributed linear algebra framework and mathematically expressive Scala DSL, programmers can quickly implement their algorithms.
Amazon Web Services
Amazon Web Services has a wide range of machine learning services. For companies and software engineers, AWS offers a wide range of tools and solutions that can be used in server farms across more than 190 nations. Government agencies, educational institutions, NGOs, and companies can all use the services. The end-users needs can be taken into account when tailoring its services.
Oryx2
Built on Apache Kafka and Apache Spark, it is a realization of the lambda architecture. For large-scale, real-time machine learning projects, it is frequently used. It also serves as a framework for creating apps, including complete packages for filtering, regression analysis, classification, and clustering.
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