Machine Learning (ML) is a revolutionary force, redefining the boundaries of technology and human interaction. At its core, ML harnesses vast amounts of data, enabling machines to learn, predict, and evolve in ways we once deemed science fiction. However navigating ML waters, especially for newcomers, is daunting. With a plethora of models, libraries, and terminologies, it's unclear where to start to get the concept.
Machine Learning is used in a wide variety of applications, such as:
In our latest blog, we aim to demystify ML and showcase its applications. We provide a comprehensive overview of popular ML libraries, comparing their strengths, weaknesses, and best use cases.
There are a number of models that make up Machine Learning, understanding the best uses of them will help you find the right model for your project.
These models can either be used in isolation or combined to create powerful products and services. For example, Voice Assistants won’t just use Deep Learning, they will combine Deep Learning with Natural Language Processing, Recommendation Systems and even Anomaly Detection.
There are lots of models and frameworks that can be adopted to take advantage of the different types of Machine Learning available, all with their own sets of Pros and Cons.
TensorFlow is a library developed by Google. It is great for large-scale, production-ready machine learning applications but it is also extremely complex and has quite verbose syntax so can be quite overwhelming for smaller projects.
For example a factory could use TensorFlow to analyse sensor data from machinery to predict when a machine will fail and manage maintenance schedules.
Airbnb uses TensorFlow for creating personalised recommendations to users based on their previous search history and bookings.
PyTorch is an open source library developed by Facebook’s AI Research lab. Researchers tend to opt for PyTorch due to the fact that it’s fairly simple to debug and can be tweaked on the fly - if this makes it good for models that are rapidly evolving.
Naturally, as the creators fo PyTorch, Facebook heavily relies on PyTorch for personalising your news feed, auto tagging images and detecting hate speech or harmful content in posts.
Scikit-learn is a lightweight library but probably the most traditional. It is very user-friendly and has loads of documentation making it a great choice for beginners, but when we say lightweight we mean it’s not designed for deep learning or large datasets.
Scikit-learn has traditionally been used behind a lot of email providers’ spam detection. Spotify also use it for its music recommendation algorithms.
Keras is a high-level neural network API that is excellent for beginners due to its simplicity and ease of use. It is designed to enable fast experimentation with deep neural networks, but offers less control and slightly lower performance compared to other libraries or APIs.
Netflix has one of the most famous deployments of Keras; it uses the library in its’ recommendation algorithms.
ML.NET is a machine learning framework developed by Microsoft for .NET applications. It’s very versatile and can suit a number of different learning models deployed directly into .NET applications.
We recently used ML.NET in an application for predicting break-out social media trends; we’ve found ML.NET to be robust in its’ output but also flexible enough to change training models on-the-fly.
Alibaba is one of the most high-profile adopters of ML.NET; it’s used in their recommendations feature.
Probably fairer to call Watson a suite than a library; it’s probably one of the most famous Artificial Intelligence services and brings together a number of applications and tools to help organisations make predictions, automate complex processes, optimise organisations and analyse large amounts of unstructured data.
IBM Watson was the first service we used for creating a virtual customer service agent for a global technology client.
The most advanced implementation we know of is Woodside Energy, an Australian oil and gas company, that uses Watson to provide answers to technical questions based on decades of operational data.
Azure Cognitive Services is a set of APIs, SDKs, and services that make it simpler for developers to add AI such as vision, speech and language analysis to their applications.
We traditionally used Azure’s cognitive services for more advanced translations when launching clients into new regions - it offers a much better level of translation than everyday translation engines, such as Google, and whilst not as good as native translation it’s good enough to prove a business case for deploying more resouces.
Azure can be used for lots of application types; Uber uses Azure Cognitive Services to verify the identify of its drivers through facial recognition.
For complete beginners, Scikit-learn is an excellent starting point. It’s extensive documentation is very supporting and API is very beginner friendly.
Start by getting comfortable with some of the basic concepts like regression, classification, and clustering. Once you’re comfortable with these concepts, we’d suggest looking at Kera to further enhance your models; again, this is a relatively simple place to start and has an easy to use API.
This isn’t an exhaustive list of libraries but a good place to start; choosing a library largely depends on your needs and goals. Start simple with Scikit-Learn and Keras or build scalable, production-ready solutions with TensorFlow and .NET - although, if you find Python more intuitive, PyTorch might be the library for you.
The best way to learn is by doing, analysing and tweaking, and going again.
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ML Library: An ML (Machine Learning) library is a collection of pre-written code, tools, and functions that facilitate the development, training, and deployment of machine learning algorithms. These libraries are designed to help developers and data scientists streamline the process of integrating ML capabilities into their applications without having to write algorithms from scratch, e.g. TensorFlow, PyTorch, and Scikit-learn.
ML Model: An ML model refers to the output of a machine learning algorithm trained on data. It represents what the algorithm has learned from the training process. Once an ML model is trained on a specific set of data, it can start making predictions or decisions without being explicitly programmed to perform the task. These models can vary in complexity from simple linear regressions to sophisticated neural networks.
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