To put it simply, machine learning is a form of artificial intelligence that automates data analysis, allowing computers to learn through experience to perform tasks without explicit programming. It’s no secret that AI and machine learning skills are increasingly in demand. According to TechRepublic, AI is expected to create 2.3 million jobs by 2020, which will replace the 1.8 million it will eliminate. In fact, the article even acknowledges machine learning as the most in-demand AI skill today. In a market growing as rapidly as this one, there are a plethora of machine learning tools available. If you choose the one that is right for you, machine learning can make various processes faster and more efficient. Making the right choice for you and your organization can be tricky, but we will take you through a few of the most popular to help get you started.

1) TensorFlow

TensorFlow offers a JS library that helps in machine learning development. Its APIs will help you to create and train the models.

I think all the machine learning lovers who work with machine learning applications know about TensorFlow. It’s an open-source machine learning library that helps you to develop your ML models. The Google team developed it. It has a flexible scheme of tools, libraries, and resources that allows researchers and developers to build and deploy machine learning applications.

Features:

• Helps in building and training your models.
• You can also run your existing models using TensorFlow.js which is a model converter.
• It helps in neural networks.
• A full cycle deep learning system.
• Train as well as build ML models effortlessly using high-level APIs like Keras with eager execution.
• This is open-source software and highly flexible.
• It can also perform numerical computations using data flow graphs.
• Run-on GPUs and CPUs, and also on various mobile computing platforms.
• Efficiently deploy and train the model in the cloud.

2) Google Cloud ML Engine

If you’re training your classifier on plenty of data, your PC or laptop might work quite well. However, if you have millions or billions of training data? Or, the algorithm is quite sophisticated and takes a long time to properly execute? You should use Google Cloud ML Engine for your rescue. It is a hosted platform where machine learning app developers and data scientists create and run optimum quality machine, learning models.

Features:

• Provides machine learning model training, building, deep learning, and predictive modeling.
• The two services viz. prediction and training can be used independently or jointly.
• This software is widely used by enterprises, i.e., detecting clouds in a satellite image, Responding faster to emails of customers.
• It can be widely used to train a complex model.

3) Accord.NET

It is a .Net machine learning framework that is combined with image and audio processing libraries written in C#. This framework consists of multiple libraries for a wide array of applications, i.e., pattern recognition, statistical data processing, and linear algebra. It includes the Accord. Statistics, Accord.Math, and Accord.MachineLearning.

Features:

• Consists of more than 40 non-parametric and parametric estimations of statistical distributions.
• Used for creating production-grade computer audition, computer vision, signal processing, and statistics apps.
• Contains more than 35 hypothesis tests that include two-way and one-way ANOVA tests, non-parametric tests such as the Kolmogorov-Smirnov test, and many more.
• It has more than 38 kernel functions.

4) Shogun

An open-source, free machine learning library, it was first developed by Gunnar Raetsch and Soeren Sonnenburg in the year 1999. This tool is written in the C++ programming language. Literally, it offers algorithms and data structures for machine learning problems. It also supports many languages like R, Python, Java, Octave, C#, Ruby, Lua, etc.

Features:

• It mainly focuses on kernel machines like regression problems and support vector machines for classification.
• This tool is initially designed for large-scale learning.
• This tool allows linking to other machine learning libraries like LibLinear, LibSVM, SVMLight, LibOCAS, etc.
• It also provides interfaces for Lua, Python, Java, C#, Octave, Ruby, MatLab, and R.
• It can process a large amount of data such as 10 million samples.

5) Apache Singa

This machine-learning software was started by the DB System Group at the National University of Singapore in the year 2014, in collaboration with the database group of Zhejiang University. This ML software is widely used in image recognition and natural language processing. It also supports a wide gamut of popular deep learning models. It has 3 major components: IO, Core, and Model.

Features:

• Device abstraction is supported for running on hardware devices.
• Flexible architecture for scalable distributed training.
• Tensor abstraction is allowed for more advanced machine learning models.
• This tool includes enhanced IO classes for writing, reading, encoding, and decoding files and data.
• Runs on asynchronous, synchronous, and hybrid training frameworks.

6) Google ML Kit for Mobile

If you are a mobile app developer, then, Google’s Android Team brings an ML KIT that packages up the expertise of machine learning and technology to create more robust, optimized, and personalized apps to run on a device. You can use this machine learning software tool for face detection, text recognition, landmark detection, image labeling, and barcode scanning applications.

Features:

• It provides powerful technologies.
• Running on-device or in the Cloud-based on the specific requirements.
• Uses out-of-the-box software development solutions or custom models.
• The kit is an integration with Google’s Firebase mobile development platform.

7) Apache Spark MLlib

It is a scalable machine learning library and runs on Apache Mesos, Hadoop, Kubernetes, standalone, or in the cloud. In addition to this, it can access data from multiple data sources. A wide array of algorithms is included like for Classification: naive Bayes, logistic regression, Regression: generalized linear regression, Clustering: K-means, to name a few. Its workflow utilities are ML Pipeline construction, Feature transformations, ML persistence, etc.

Features:

• Hadoop data sources like HDFS, HBase, or local files can be used. So it is easy to plug into Hadoop workflows.
• Ease of use. It can be usable in Java, Scala, Python, and R.
• MLlib fits into Spark’s APIs and interoperates with NumPy in Python and R libraries.
• It contains high-quality algorithms and outperforms better than MapReduce.

8) Apple’s Core ML

Core ML by Apple is a machine learning-based framework that helps you to integrate machine learning models into your mobile app. You have to drop the machine learning model file into your project, and the Xcode builds a Swift wrapper class or Objective-C automatically. Using this model is straightforward and can leverage each GPU and CPU for maximum performance.

Features:

• Acts as a foundation for domain-specific frameworks and functionality.
• Core ML easily supports Computer Vision for precise image analysis, GameplayKit for evaluating learned decision trees, and Natural Language for natural language processing.
• It is carefully optimized for on-device performance.
• It builds on top of low-level primitives.

9) Oryx 2

It is a realization of the lambda architecture and built on Apache Kafka and Apache Spark. It is widely used for large-scale machine learning on a real-time basis. It is a framework for building apps including end-to-end applications for filtering, packaged, regression, classification, and clustering. The latest version of this tool is Oryx 2.8.0.

Features:

• It has three tiers: specialization on top providing ML abstractions, generic lambda architecture tier, end-to-end implementation of the same standard ML algorithms.
• Oryx 2 is an upgraded version of the original Oryx 1 project.
• It consists of three side-by-side cooperating layers as speed layer, batch layer, and serving layer.
• There is also a data transport layer that moves data between the layers and receives input from external sources.

10) Apache Mahout

Apache Mahout is a mathematically expressive Scala DSL and distributed linear algebra framework. It is an open-source and free project of the Apache Software Foundation. The main goal of this framework is to implement an algorithm promptly for mathematicians, data scientists, and statisticians.

Features:

• Implementing machine learning techniques including recommendation, clustering, and classification.
• An extensible framework for building scalable algorithms.
• It includes matrix and vector libraries.
• Run-on top of Apache Hadoop using the MapReduce paradigm.