Want to master artificial intelligence (AI) techniques? A new book, The Supervised Learning Workshop, teaches you how to create machine-learning models using the Python programming language. A conversation with the co-author, Blaine Bateman.
Blaine Bateman is a business strategy consultant, helping companies identify growth strategies and opportunities.
Several years ago he decided to focus on data analysis or, more accurately, predictive analytics using machine learning.
“I started to see that clients had lots of data, frequently they didn’t know anything about it and they weren’t using it,” he says. “At the same time, I started to see that AI and machine learning were really on the uptick.”
Machine learning work is also rewarding, he says: “You build stuff and when you get it to work, you do something that helps someone.”
But it is not all fun: there is a lot of “data wrangling”, preparatory work to get the data ready for modelling.
First, the data may need to be integrated if it comes from several sources, and it may need to be scaled. It also pays to study the data, to discover as much as possible about it before modelling. All this takes time.
“Everyone likes the idea that you shovel data into a machine-learning black box and insights come out, but it is not that simple,” says Bateman.
Coming of age
AI and machine learning are terms commonly always mentioned together although machine learning is, in fact, a subset of AI.
There is also no real intelligence here, says Bateman. Machine learning, or what he calls predictive analytics, is the application of tools, algorithms and methodologies to train models.
“That is the learning part and it is using machine - a computer,” he says. “AI sounds a lot cooler but the vast majority of times you see the two, it is one and the same thing.”
AI is also not a new topic: neural networks, genetic algorithms and fuzzy logic were the subjects of much attention in the 1980s and '90s. But developments in recent years has caused AI to finally hit its stride.
One factor is the maturity of silicon for AI, another is the advent of cloud computing. Bateman also highlights how the AI and machine-learning community embraced the open-source software movement. “It means there is a tremendous amount of commercial-scale work being done using open-source software,” he says.
Google’s TensorFlow, an example of open-source software, is one of the most used libraries for neural networks, while Keras is a software layer that sits on top, simplifying the use of TensorFlow in a Python environment.
“Coding languages such as Python and R have been around a long time and, with the open-source movement, these have grown and been extended into incredibly capable platforms,” says Bateman.
Another important catalyst for AI has been the development of e-learning, or massively open online courses (MOOC).
People used to go to college, learn a skill, enter industry and learn on the job before developing a career. “Now, people are jump-starting that,” he says.
There is an entire industry of people using online courses to learn as fast as possible. “Which is the reason for the books like the one I’ve participated on,” says Bateman.
Supervised learning
Supervised learning refers to data which has a characteristic or property that is known in advance, also referred to as labelled data.
Examples of such data could be numerous images of car registration plates to train an automated road-tolling system, or labelled images of lung abnormalities to train a medical scanning tool.
“We train a model by giving it a picture and telling it the answer,” says Bateman. “Then, once the model is built, you can translate a new picture into an answer.”
There is also unsupervised learning which refers to another aspect of machine learning. Here, data is applied to a clustering algorithm, for example, the MNIST handwritten digits database used to train algorithms to recognise ZIP or postcodes.
The MNIST database can be used for supervised learning, training a model to recognise each of the digits. But in unsupervised learning, the algorithm segregates the digits into clusters without being told what they are.
There are sophisticated clustering methods such as the uniform manifold approximation and projection (UMAP) approach that can reduce complex data sets into smaller dimensions. “It can take up to 80 dimensions and project them onto three and oftentimes find meaningful patterns,” he says.
Yet so far unsupervised learning is not used that much whereas supervised learning accounts for over 80 per cent of all machine learning applications used today, says Bateman.
Book
Packt Publishing wanted to issue a new edition of its machine learning book that included the latest supervised-learning practices. The publisher approached Bateman after seeing his work in the open-source community.
The resulting book - The Supervised Learning Workshop - is aimed at undergraduates and engineers. “Since it jumps into supervised learning, the expectation is that you have some coding skills and know enough Python to work through the exercises,” says Bateman.
The book uses Jupyter Notebooks, an open-source web application for developing Python code. “A lot of people use it to do small projects,” he says.
The topics addressed in the book can all be run using a decent laptop. “A huge amount of people working in AI are working on laptops; it is definitely doable with today’s technology,” he says.
And for larger data sets and bigger problems, there is always the cloud service providers such as Amazon Web Services, Google, Microsoft and others.
After a short introduction covering supervised and unsupervised learning, the book starts with linear regression, which remains an extremely important tool in machine learning.
His advice to students is to build a linear model first and see how well it performs. “That gives you a baseline,” says Bateman.
The topic of gradient-descent is then introduced, a technique used to train more sophisticated algorithms such as neural networks. Further into the book, more sophisticated techniques are introduced.
“The most sophisticated thing we talk about is ways to combine these algorithms into ensembles,” says Bateman.
Ensembling refers to using several less powerful models - what Bateman calls ‘weak learners’ - that are combined in some way, their results may be averaged or their outputs voted on.
Ensembling provides superior results compared with using a single model, even a sophisticated one.
Bateman feels lucky to be working in the field of machine learning.
“We have this explosion of freely-available technology that you can use on a laptop to solve amazing problems,” he says. “It is an astonishing time to be a technologist.”