Artificial intelligence and machine learning tools have become the flavor of the day. Almost everywhere, you come across excellent examples of machine learning. In this article, we shall explain the concept of machine learning and see how it is useful to us in our daily lives. We shall also go a bit into the technical aspects and look at some of the most popular machine learning tools for beginners as well as experienced persons.
You must have heard this quote, “A carpenter is only as good as his tools.”
The same logic applies here, as well. The better the machine learning tools you use, the better is the end product you have on your hands.
Machine Learning – The Concept
We shall not go into the substantial definitions of machine learning that can confuse the best of people. We should leave it to the classrooms. Here are some simple examples of machine learning that we encounter almost every day.
Example – 1
Almost everyone buys something like a digital camera on e-commerce retail websites like Amazon. Before making the purchase, you browse the internet a hundred times, looking for the right configurations. After a couple of days of intensive search, you find ads showing various digital cameras whenever you visit any other website or search engine.
It is an ideal example of machine learning. Your computer or phone understands that you are looking for a digital camera. It endeavors to make it easy for you by displaying these ads automatically without you asking.
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Example 2
Almost all mobile apps today use machine learning tools and frameworks to deliver the best experience to their users. Social media uses machine learning tools more than anyone else. The Facebook Photo feature is an excellent example of AI and machine learning. Whenever you upload a photo on Facebook, it recognizes the person in the picture and flashes their name when you move the cursor over their faces. Of course, the person should have a Facebook account for the purpose. It is also an excellent example of machine learning.
Different Types of Machine Learning
Before going on to look at the machine learning tools list, we shall first glance through different types of machine learning.
- Supervised Machine Learning – This type of machine learning uses past data for making predictions. One of the best examples is the spam filtering of emails. Machine algorithms decide whether an email is spam or not based on the previous data.
- Unsupervised Machine Learning – It is the opposite of supervised machine learning. It finds hidden patterns, the best example of which is the Facebook Photo feature that we discussed previously.
- Reinforcement Machine Learning – This category of machine learning improves or increases efficiency.
After going through these examples, you must have an idea of what machine learning is. Now, we shall look at 19 of the most popular machine learning tools in use today. Each tool from this machine learning tools list is easy to use and understand. Most of them qualify as machine learning tools for beginners.
1. PyTorch – Versatile and easy to use
PyTorch is a free machine learning tool that can be used on various cloud platforms. This machine tool works on a hybrid front-end, and thereby, it is easy to use. The Torch-based Python machine learning library helps in building neural networks using the Autograd Module. It also provides a variety of optimization algorithms for constructing these neural networks.
2. Weka 3 – Excellent machine learning tool for data mining
Weka 3 is one of the best machine learning algorithms that help in data mining. Almost every data scientist uses this tool for various activities like data preparation, classification, clustering, regression, visualization and association rules mining. One of the highlights of this machine tool for beginners is that it uses algorithms that are easy to understand. As it is an ideal machine tool for providing online courses for training, it is popular among students.
3. Apache Mahout – Ideal for statisticians and mathematicians
Apache Mahout follows the Distributed linear Algebra framework and helps statisticians, data scientists, and mathematicians in executing their algorithms. This free machine learning tool provides algorithms for clustering, regression, pre-processors, DLA, and recommenders. One of the best aspects of this machine learning tool is that it is extensible but straightforward and works for large data sets. However, it also comes with certain drawbacks, like missing algorithms and documentation.
4. Shogun – Best in this machine learning tools list for handling large volumes
Shogun is a free and open-source machine learning library in C++ language that offers data structures and algorithms for machine learning problems. This tool supports languages like Ruby, C#, Java, Octave, R, Lua, Python, and Ruby. The best feature of this machine learning tool is that it can produce large quantities of data, such as more than 10 million samples.
5. Rapid Miner – One of the best machine learning tools for beginners
Rapid Miner is one of the most expensive machine learning tools but an excellent one for deep learning, text mining, data preparation, and predictive analytics. It uses GUI to design and implement analytical workflows. Extensible through plugins, this tool is an easy one to use because it does not require any programming skills.
6. Scikit Learn – Easy to understand and use
Python learners can use this machine learning tool as it provides an extensive library for the programming language. This machine learning tool for beginners provides excellent models and algorithms for clustering, model selection, classification, dimensional reduction, and pre-processing. This easily understandable machine learning tool is perfect for data analysis and data mining. It is a free tool that allows changing of parameters for a specific algorithm while calling objects.
7. TensorFlow – Challenging tool to learn but great to work with
TensorFlow is another free machine learning tool in this machine learning tools list that helps in training and building your models. This tool provides a JavaScript library to help in machine learning. Users can utilize the TensorFlow.js, a model converter to run existing models. One can install this machine learning tool through NPM or by script tags. One of the disadvantages of this machine learning tool and frameworks is that it is a difficult tool to learn for beginners.
8. KNIME – Great utility for business entities
KNIME is a free machine learning tool that is easy to learn. It is an ideal tool for data reporting, analytics, and integration platforms. It uses the data pipelining concept to combine different components for data mining and machine learning. The advantage of this ML tool is that it can integrate codes of programming languages like Java, JavaScript, R, Python, C, and C++. These qualities make it the best in this machine learning tools list for financial data analysis, business intelligence, and CRM.
9. Accord.Net – Supports an array of applications
Accord.Net is a .net machine learning tool and framework that combines image and audio processing libraries in C# language. This framework consists of a wide variety of applications that include statistical data processing, linear algebra, and pattern recognition. It is excellent for creating analytical apps, production-grade computer auditions, and computer vision. This framework comprises of more than 38 kernel functions.
10. Colab – excellent machine learning tool and framework for research
The advantage of Colab is that it is accessible from Google Drive. Google Colab, a cloud service that supports Python, helps in building machine learning applications using the libraries of TensorFlow, OpenCV, PyTorch, and Keras. This free tool is one of the best machine learning tools for beginners as it helps in machine learning education and assists in machine learning research.
11. Google Cloud ML Engine – The quickest in the business
Google Cloud ML Engine is an excellent machine tool that can handle billions of training data. This tool can deal with sophisticated algorithms and work with data that take a long time for proper execution. Data scientists and machine learning app developers use this hosted platform to run an optimum quality machine learning model. This tool is useful for detecting clouds in satellite images. Business enterprises use this tool as it responds quickly to customers’ emails.
12. Amazon Machine Learning – A widely accepted machine learning tool
AML is a multipurpose machine learning tool that can be used by all web and mobile app developers. This managed service can integrate data from multiple sources like RDS, Amazon S3, and RedShift. It supports three categories of models, regression, binary classification, and multi-class classification. Apart from providing wizards and visualization tools, it permits users to create data source objects from Amazon RedShift and MySQL databases.
13. Oryx 2 – A high-quality tool for building apps
This machine learning tool and framework is an upgraded version of the Oryx 1 project. This framework is excellent for building end-to-end apps for clustering, classification, regression, packaging, and filtering. The latest version of the tool, Oryx 2.8.0 consists of three side-by-side layers, the serving layer, batch layer, and speed layer. Built on Apache Spark and Kafka, this machine learning tool is a realization of lambda architecture.
14. Apache Spark MLlib – Capable of access extensive data
Apache Spark MLlib is a scalable ML library that can access data from various sources. It can run as a stand-alone application in the cloud or on Hadoop, Apache Mesos, and Kubernetes. One of the best features of this machine learning tool is that it is usable in R, Python, Java, and Scala. When compared to MapReduce, this tool contains high-quality algorithms and outperforms it easily.
15. Apache Singa – A flexible tool in its own right
Apache Singa is an image recognition and natural language processing ML software started by DB System Group in collaboration with Zhejiang University in Singapore in 2014. Having major components like Model, IO, and Core, this machine learning tool supports an extensive range of deep learning models. It has a flexible architecture for scalable distributed training and can run on synchronous, asynchronous, and hybrid training frameworks.
16. Apple’s Core ML – Simple machine learning tool for beginners
Core ML is one of the ideal machine learning tools for beginners that help to integrate other ML models into your mobile app. This model is an uncomplicated tool that can leverage CPUs and GPUs for optimum performance. Core ML supports Natural Language processing, Gameplay Kit and Computer Vision. This carefully-optimized tool acts as a foundation for domain-specific frameworks.
17. Amazon SageMaker – Great for making predictions
Amazon SageMaker makes data mining and ML more user-friendly, whereby it becomes convenient for those who make market predictions and look for mining details. It is an excellent machine learning tool for predictive analytics. Hence, you find this tool used by specific departments that support ML development as well as deployment. One of the primary disadvantages is that it does not serve multiple purposes though it is excellent for carrying out specific tasks. It is not the ideal tool to handle a large quantity of data because it is not as quick as some of the machine learning tools discussed above.
18. Google ML Kit for Mobile – Tool that you experience almost daily
Mobile apps are becoming the order of the day. Mobile app developers will love this machine learning tool as it packages both the expertise of ML and technology to create optimized and personalized apps to run on a smart device. This tool is the perfect one for face detection, landmark detection, barcode scanning, text recognition, and image labeling. Almost all of these features are available on smartphones these days. The best part of this machine learning tool is that it can run on-device as well as in a cloud-based environment.
19. Keras.io – A fast tool for prototyping
Keras.io is a Python deep learning library. Keras is one of the most user-friendly tools that are perfect for quick and easy prototyping. This tool can run on both your CPU and GPU. As it assists recurrent networks, it supports a combination of two networks. This free machine tool is extensible and supports convolution networks. Keras, written in Python language, functions as an API for neural networks. One of the significant drawbacks of Keras.io is that the user should know Theano, CNTK, or TensorFlow to use it.
Final words
Machine learning is an exciting topic. However, you should use the best machine learning tools and frameworks to get the best out of AI and ML. Most of the applications in this machine learning tool list are free and open-source, thereby enabling users to customize them to suit their individual needs. Some of the machine learning tools like TensorFlow and Keras.io are ideal for neural networks. Tools like Scikit Learn and PyTorch support Python programming and hence, are popular tools for ML.
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