In our increasingly digital world, data is quite literally everywhere. Every click, every swipe, every video and every word can be converted into very abundant (and, in the right hands, lucrative) data.
But there isn’t enough time in the world for us humans to sift through all of this data, understand it, and use it to its full advantage. That’s why we have machine learning, giving computers the ability to not only automate data analysis, but do it in such a way that it can “learn” through experiences and context rather than simple coding — much in the same way we humans learn.
Machine Learning Tools to Know
- Apache Mahout
- AWS Machine Learning
- Google Cloud AutoML
- IBM Watson Studio
- Microsoft Azure Machine Learning
- Vertex AI
Giving computers the ability to develop more human-like learning capabilities makes them useful in not just novel things like generating images or translating cat purrs, but in a variety of industries as well, including finance, healthcare, education and even archaeology.
What Is Machine Learning?
Machine learning is a subset of artificial intelligence that uses statistics, trial and error, and mountains of data to learn a specific task without ever having to be specifically programmed to do that task.
While most computer programs rely on code to tell them what to do and how to do it, computers that use machine learning use tacit knowledge — the knowledge we gain from personal experience or context. This process relies on algorithms and models, or statistical equations that are developed over time based on the data at hand. The learning process, also known as training, involves identifying patterns in data, and then optimizing those findings through both trial and error and feedback.
Because machine learning systems can learn from experience, just as humans do, they don’t have to rely on billions of lines of code. And their ability to use tacit knowledge means they can make connections, discover patterns and even make predictions based on what it can extract from data.
In short: Machine learning puts the onus of problem-solving on computers, rather than humans. These algorithms are capable of parsing through enormous amounts of information and finding patterns no human could ever do on their own, making them especially useful in building recommendation engines, accurately predicting online search patterns and fraud detection, among other things.
The Importance of Machine Learning Tools
Like all systems that use AI, machine learning requires algorithms to act as a sort of guide for the system. A machine learning model is trained with an algorithm to recognize patterns and provide predictions. And as new data is fed into these algorithms, they learn and improve their performance, developing a sort of intelligence over time.
There are hundreds of algorithms computers can use based on things like data size and diversity, but they can largely be put into four different categories, depending on how much human intervention is required to ensure their accuracy over time. And these algorithms are created using machine learning tools and software.
Of course, in an area as vast and complex as this, there is no jack of all trades — no one model can fix everything or do everything. So there are lots of machine learning tools out there.
Listed below are some of the most popular ones.
Machine Learning Tools to Know
Developed by the Apache Software Foundation, Mahout is an open-source library of machine learning algorithms, implemented on top of Apache Hadoop. It is most commonly used by mathematicians, data scientists and statisticians to quickly find meaningful patterns in very large data sets. In practice, it is especially useful in building intelligent applications that can learn from user behavior and make recommendations accordingly.
AWS Machine Learning
AWS Machine Learning offers a variety of tools designed to help developers discover patterns in user data through algorithms, construct mathematical models based on those patterns, and generate predictions from those models. Some of its free product offerings include Amazon Rekognition, which identifies objects, people, text and activities in images and video; and Amazon SageMaker, which helps developers and data scientists build, train and deploy machine learning models for any use case.
BigML provides machine learning algorithms that allow users to load their own data sets, build and share their models, train and evaluate their models and generate new predictions either singularly or in a batch. And all of the predictive models created on BigML come with interactive visualizations and explainability features that make them more interpretable. Today, the platform is used across a variety of industries, from aerospace to healthcare, according to the company.
Google’s Colab is a cloud service that helps developers build machine learning applications using the libraries of PyTorch, TensorFlow, Keras and OpenCV — some of which will be further discussed later in this piece. It allows users to combine this code with rich text, images, HTML and more into a single document in order to build and train machine learning models. These models can then be stored on a Google Drive, shared and edited by others.
Google Cloud AutoML
Based on the tech giant’s state-of-the-art transfer learning and neural architecture search technology, Google Cloud AutoML is a collection of machine learning products that helps developers train high-quality models for whatever they need them for, even if they have limited machine learning experience. The tool allows users to evaluate, enhance and deploy their models, as well as train. They can also generate predictions on their trained models and securely store whatever data they need in the cloud.
IBM Watson Studio
IBM’s Watson is among the most familiar players in not just machine learning, but also cognitive computing and artificial intelligence in general since it won a game of Jeopardy! in 2011 against two human champions. Today, the IBM Watson Studio helps developers put their machine learning and deep learning models into production, offering tools for data analysis and visualization, as well as cleaning and shaping data.
Microsoft Azure Machine Learning
Azure Machine Learning offers everything developers need to build, test and deploy their machine learning models, placing an emphasis on security. Its collaborative, drag-and-drop design takes developers throughout the entire machine learning process, and comes with features for data exploration preparation, model training and development, model validation, as well as continuous monitoring and management of the model. Plus, the tool requires no programming — rather, it visually connects the data sets and modules to help users build their predictive analysis model.
Short for Open Neural Networks Library, OpenNN is a software library that implements neural networks, a key area of deep machine learning research. It is written in C++ programming language and the entire library can be downloaded for free from GitHub or SourceForge.
PyTorch is an open-source tool that helps with deep learning and machine learning model development. The platform offers tensor computing, neural networks, and a host of machine learning libraries and tools. PyTorch also has additional wrappers — PyTorch Lightning and PyTorch Ignite — both of which are meant primarily to expand on research capabilities and diminish the need for redundant code.
Scikit-learn is among the most used libraries for machine learning. It is Python-based, and contains an array of tools for machine learning and statistical modeling, including classification, regression and model selecting. Because scikit-learn’s documentation is known for being detailed and easily readable, both beginners and experts alike are able to unwrap the code and gain deeper insight into their models. And because it is an open-source library with an active community, it is a go-to place to ask questions and learn more about machine learning.
Shogun is a free, open-source machine learning software library that offers numerous algorithms and data structures for machine learning problems. It also offers interfaces for many languages, including Python, R, Java, Octave and Ruby. This is one of the more “underrated” libraries for machine learning, according to Emmett Boudreau, a popular contributor to the Towards Data Science blog — likely due to its smaller user base and maintainer list. But Boudreau said the Shogun library is more established language-wise, which leads to more accessibility both cross-platform and in different applications.
Initially developed by Google, TensorFlow is an open-source machine learning framework, offering a variety of tools, libraries and resources that allow users to build, train and deploy their own machine learning models. It supports a wide range of solutions, including natural language processing, computer vision, predictive machine learning and reinforcement learning. While TensorFlow does offer some pre-built models for simpler solutions, it mostly requires developers to work closely with a given model’s code, which means they can achieve full control in training the model from scratch. TensorFlow also has a deep learning API for Keras, called tf.keras.
Also a product of Google, Vertex AI unifies several processes within the machine learning workflow, enabling users to train their machine learning models, host those models within the cloud and use their models to reach conclusions about large amounts of data. While Vertex AI comes with pre-trained models, users can also generate their own models by leveraging Python-based toolkits like PyTorch, Scikit-lean and TensorFlow.
Weka is a free collection of machine learning algorithms for data mining tasks, offering tools for data preparation, classification, regression, clustering, association rules mining and visualization. When a data set is fed in Weka, it explores the hyperparameter settings for several algorithms and recommends the most preferred one using a fully automated approach. Developed at the University of Waikato in New Zealand, Weka was named after a flightless bird found only on the island that is known for its inquisitive nature.
Short for Extreme Gradient Boosting, XGBoost is an open-source machine learning software library. The platform provides parallel tree boosting in order to solve many data science issues quickly, meaning several tree-based algorithms can be used to achieve the optimal model sequence. Plus, with gradient boosting, XGBoost grows the trees one after another so that the following trees can learn from the weaknesses and mistakes of the previous ones, as well as borrow information from the previous tree model.