The Best Machine Learning Tools - Expert's Top Picks
The Best Machine Learning Tools

The Best Machine Learning Tools – Expert’s Top Picks

Last updated on 16th Jul 2020, Blog, General

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  • Machine learning tools are algorithmic applications of artificial intelligence that give systems the ability to learn and improve without ample human input; similar concepts are data mining and predictive modeling.
  • They allow software to become more accurate in predicting outcomes without being explicitly programmed. The idea is that a model or algorithm is used to get data from the world, and that data is fed back into the model so that it improves over time. It’s called machine learning because the model “learns” as it is fed more and more data.
  • They can be used, for example, to build recommendation engines, predict search patterns, filter spam, build news feeds, detect fraud and security threats, and much more.
  • There are four types of machine learning algorithms: supervised, unsupervised, semi-supervised, and reinforced.
  • Supervised algorithms are machine learning tools with training wheels. They require a person to program both the input and the desired output, as well as provide feedback as to the accuracy of the end results.
  • Unsupervised algorithms require very little human intervention by instead using an approach called “deep learning” to review massive banks of data and arrive at conclusions based on previous examples of training data;
  • they are, therefore, generally used for more complex processing tasks such as image recognition, speech-to-text, and natural language generation.
  • Semi-supervised algorithms tend to fall in the middle ground.
  • Reinforced algorithms force models to repeat a process until it produces the most favorable outcomes. Attempts that produce these favorable outcomes are rewarded and attempts that produce unfavorable results are penalized until the algorithm learns the optimal process.

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    This list of top machine learning tools is just the start and will continue to be updated. So, let’s begin:

    TensorFlow

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    • 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 the machine learning applications know about the TensorFlow. It’s an open source machine learning library which 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 network.
    • 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 an 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.

    You can also use it in 2 ways, i.e. by installing through NPM or by script tags.

    • Tool Cost/Plan Details: Free

    Google Cloud ML Engine

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    • If you’re training your classifier on a 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 take a long time in proper execution?
    • 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.

    Here are top 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.

     Accord.NET

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    • It is a .Net machine learning framework which 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 estimation 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.

    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.

     Apache Singa

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    • 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.
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    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.

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