The terms Data Science, Artificial Intelligence, and Machine Learning are used interchangeably as they are connected, even though Data Science courses vary significantly. Each has its specific applications. There are overlaps among these domains, but each of these terms represents some unique features in solutions and products of the current age.
In the following sections, we shall look at some of the excellent known tools in each and attempt to highlight some of their key features.When you take up the best data science courses offered by the best institutes, you would also master the tools and techniques of Data Science, AI and ML.
Before we dive in, let’s briefly set the context right about Artificial Intelligence, Machine Learning and Data Science.
What is Artificial Intelligence?
While the term Artificial Intelligence carries a lot of weightage, it generally points to the expected behaviour of an intelligent machine. In practice, several technologies, either individually or together in some combination, make artificial intelligence possible.
Simply put, AI attempts to bring the human traits of reasoning and self-awareness into machines. Like human beings, AI aims to have devices that can learn from experience and improve with constant feedback. AI thus depends on underlying technologies of Machine Learning, Deep Learning, Natural Language Processing, computer vision, among others, to achieve this goal.
What is Machine Learning?
Machine learning is one of the building blocks of Artificial Intelligence. Using Machine Learning methods and algorithms, machines can learn and improve from experience over time. This learning can be assisted or unaided depending on the particular algorithm and learning data in use. This is against the traditional practice of programming a machine to do a particular job. Here the machine goes through a massive swathe of data to recognize the patterns and establish relationships based on already known information. Once the device is ready, it can use the inputs to find out the results by itself.
What is Data Science?
Data Science is a much broader field from which Artificial Intelligence and Machine Learning and other Artificial Intelligence building blocks borrow knowledge. It is a science of data systems and processes aimed at deriving valuable insights from data, no matter how massive or unstructured it is. It is science based on mathematical principles that identify patterns and relationships within data.
The information extracted through techniques defined in data science is used to drive business decisions.
Here is a list that includes some of the top tools that help in implementing Machine Learning models
An open-source library created by Google that lets you build machine learning and deep learning models. TensorFlow can also create dataflow graphs that show how data moves through a graph, among other capabilities.
PyTorch, a Facebook developed an open-source framework based on Torch, is another well-known tool for building and training machine learning models. Pytorch is well suited for research based on deep learning using accelerated GPUs.
H2O is an open-source, fast, distributed, and scalable machine learning platform. It incorporates various useful statistical and machine learning algorithms, including gradient, generalised linear models, boosted machines and more.
H2O uses distributed computing to achieve speed and scalability by distributing data across clusters and storing it in a unique compressed column format.
Apache Mahout is an open-source platform for building Machine Learning applications primarily focused on Linear Algebra. Additionally, Mahout provides convenient Java/Scala libraries for complex mathematical operations.
Apache Mahout offers algorithms that help create machine learning models based on classification, clustering, collaborative filtering and evolutionary programming. Apache Mahout is built on Apache Hadoop for scalability.
With distributed computing and GPU acceleration, machine learning model building is considerably faster.
Apache Spark MLib
Apache Spark is an open-source framework that provides a high degree of fault tolerance and data parallelism using cluster computing.
Apache Spark MLib offers linear algebra packages like Breeze and Netlib-java. The MLib is also optimised to handle both batch and streaming data with equal ease.
The MLib framework offers ML algorithms based on classification, clustering, regression and collaborative filtering. Other features of MLib include feature extraction, dimensionality reduction, and transformation. It supports interfaces in Java, Python, R, Scala and SQL.
Top Data Science Tools
One of the most popular data science tools with a proven track record designed explicitly for statistical computations. SAS is proprietary software that combines great technology with customised solutions and unmatched support.
SAS offers several statistical libraries and tools that can be used for data modeling. The downside to SAS is, it will burn a hole in your pocket.
Apache Spark is a powerful analytics engine and is one of the most used Data Science tools. Spark is adept at handling both batch processing and stream processing.
Apache Spark exposes several APIs that help data scientists with access to data for Machine Learning purposes. Spark is an improvement over Hadoop and performs close to 100 times faster than MapReduce.
Another widely used open-source data science tool that provides a fully interactive cloud-based environment with an appealing UI. BigML primarily provides Machine Learning algorithms over the cloud for industrial settings.
BigML facilitates the use of ML algorithms through REST APIs that can be used across the business on demand. Another prominent feature of BigML is predictive modeling. The ML algorithms on offer include algorithms based on clustering, time-series forecasting, classification and more.
The excellent part is that BigML lets you create a free account based on your data needs.
BigML allows for automatic tuning of hyperparameter models and even automation of workflow.
Top Natural Language Processing tools
MonkeyLearn is a user-friendly Natural Language Processing platform that helps you with valuable insights from your textual data like system logs or customer conversations on social media. It offers a free pretrained model to perform advanced textual analysis, including sentiment analysis, keyword extraction, and topic classification. It is possible to build a more customised model suited to your business needs using MonkeyLearn.
The tool also lets you connect your model to favorite applications like Google Sheets, Excel, Zendesk and more with absolutely no coding skills required.
Aylien is an API based on the SaaS model. It allows for deep learning and natural language processing algorithms to analyze massive volumes of text-based data. You could even explore content from news portals and social media sites. Text summarization, entity extraction, article extraction, and sentiment analysis are some of the features offered by Aylien.
IBM Watson, a cloud-based suite of AI services, offers a Natural Language Processing service called Natural Language Understanding. It allows keyword identification and extraction. It also expands its capabilities to categories, entities and emotions.
Google offers its natural language processing capabilities in the form of Google Cloud Natural Language API. It provides pre-trained models that allow for sentiment analysis, classification, entity extraction and more. It also offers AutoML Natural Language for you to build your own language-based machine learning models.
A Natural Language Processing service from Amazon, Comprehend is an offering integrated into the AWS suite. It is an API-based offering that offers sentiment analysis, entity recognition, topic modelling and more.
Amazon has a pre-tailored NLP library for medical terms called Amazon Comprehend Medical. It allows you to carry out advanced analysis and build machine learning models based on medical terms.
A Python library for Natural Language Processing, NLTK is one of the widely used tools in its domain. If you are interested in gaining hands-on low-level NLP experience, NLTK is the way to go. NLTK provides many NLP tasks like tokenization, stemming, tagging, parsing, classification and more.
Once you get the hang of Machine Learning, NLP, and data science fundamentals, it is easy to pick up the above-listed tools to achieve what you intend to.
The general rule is if you need dedicated support for these AI and Data Science tools, you need to go in for proprietary software. Although you have considerable community-based support backing the open-source tools and frameworks, you won’t get the quality and dedicated support you find in proprietary software.