What Is Machine Learning? What Are The Applications Of Machine Learning?

What Is Machine Learning?

Machine Learning has become increasingly popular, particularly in relation to artificial intelligence and so-called Big Data.

Machine Learning is a subset of Artificial Intelligence, using algorithms to learn about data. Machine Learning algorithms are able to enhance themselves. The main focus of Machine Learning is the creation of computer programs which are able to teach themselves when faced with new data.

What Type Of Data Does Machine Learning Deal With?

Machine Learning refers to a broad range of algorithms. Algorithms such as Gradient Boosting and Random Forest are used on large data sets, depending on use case. Typically a data scientist would determine the best type of algorithm to use.

Smaller data sets, on the other hand, might use statistical techniques such as Statistical Modelling.

What Are The Applications for Machine Learning?

Machine Learning is already in production across a number of different verticals. Some applications of Machine Learning that you should know about are:

  • Finance and Banking
    • Machine Learning is being used to determine, in real time, customers who are likely to default on payments, or those who have become a victim of fraud. Machine Learning will be able to give banks and financial services institutions a better idea of who should be given mortgages, loans and credit cards, potentially eliminating human biases.
  • Healthcare
    • Machine Learning is being used in Healthcare to assess likelihood that a patient may have (or if they may in the future be diagnosed with) a disease, based on past data. Machine Learning may also have important implications for preventative healthcare.
  • Retail
    • Machine Learning is used in retail to build accurate maps of customer identity, identify popular products, and identify which combinations of products tend to be bought together. This can help retailers develop and maintain customer loyalty, and provide a first-class customer experience.

As businesses generate more and more data, and data scientists create models which are capable of processing this data with ever more accuracy, Machine Learning will provide strategic advantages in any number of industries.

Where Is Machine Learning Coming From?

A good question! There are a number of different disciplines that are coming together to work on Machine Learning, each with their own traditions and discourse.

  • Computational Neuroscience – the study of brain function in terms of the information processing properties of the structures that make up the nervous system. Artificial Neural Networks are inspired by the biological neural networks that constitute animal brains. Computational neuroscience has proved particularly useful in applications that do not respond well to traditional rule-based algorithms.
  • Statistics – Both statistics and machine learning work with data to solve problems, but approach it in different ways. Machine Learning emphasizes prediction, where statistics focuses on estimation and inference. Statistics and machine learning can be considered two sides of the same coin: computer scientists design algorithms that will be used as part of software packages, and statisticians provide the mathematical foundation for this research to take place.
  • Adaptive Control theory – Adaptive control attempts to react to a control system with parameters that vary. For example, an aircraft decreases in mass as a result of fuel consumption, so a control law is needed that is able to adapt to those changing conditions. In robotics, a robot has sensors which need to react to changing environments.
  • Psychology – Machine Learning is used routinely in data analysis in psychology and neuroscience. Machine Learning has, for example, been used on data -including IQ, personality traits, and blood makers to create predictive models of teenage binge drinking.
  • Artificial Intelligence – AI and Machine Learning have always been strongly linked. Arthur Samuel’s was the world’s first self-learning program, and as such a very early demonstration of the fundamental concept of AI. Samuel believed that teaching computers to play games was a good technique for teaching computers to solve general problems. More recently, John Ross Quinlan’s research on decision tree algorithms has helped data scientists generate decision trees from datasets.

The Future of Machine Learning

Data scientists are working to create a future where automated techniques can create patterns that a human observer may have missed. Data scientists want to write algorithms for completing tasks, and to do so in the most efficient (and therefore inexpensive) way possible.

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What Artificial Intelligence Means for Marketers

There’s growing excitement – admittedly at times borderline hype – about what artificial intelligence can and will do for businesses. While speculation abounds among pundits, journalists and ‘thought leaders’ surrounding the impact that AI will have on jobs (CBInsights predicts 10m jobs are at risk in the next 5-10 years) there’s relatively little analysis of the tangible effect AI will have on marketer’s day-to-day work, and the opportunity to ‘upskill’ us all.

Today’s marketers will benefit by navigating an increasingly AI-centric (and AI-literate) world where bots, intelligent software and machine learning play an increased role in the marketing function. To help you cut through the noise, here are some tangible examples of where AI is likely to become a relevant part of the modern marketers’ workflow, as well as ideas on how to better understand and qualify the impact that AI can have on your business.

Data Analysis for the Masses

Data analysis and processing is becoming far less laborious, and much more effective. In the past, brands and agencies have needed to employ teams of data analysts whose job was to build segments based on observed patterns in first-party data, and often merge second and third party data.

Building segments is time-consuming, error-prone, with segments often out of date by the time they’ve been created. Challenges compound when the half-life of a cookie is short, many marketers still lack a single customer view, and data match rates between marketing and ad platforms comes into play. To conduct data analysis and run intelligent campaigns at scale, marketers and their analysts often rely, with mixed success, on merging data from numerous silos. The result; marketers still lack visibility into how reliable their data is, and few quantitative tools to benchmark data quality against.

Thanks to open source machine learning libraries and developer tools for data scientists, along with the cloud computing infrastructure that supports AI and machine learning – think Amazon AWS plus Apache Spark, or Google Cloud’s Machine Learning Engine and Microsoft’s Azure Machine Learning Studio – data science is becoming democratised. This means data scientists spend less time piping and cleaning data, and more time solving meaningful problems with data. Advice: get to know, love, and empower, your data scientists. The more they understand your marketing and business needs, and can access the right data, the more they will make you shine.

Understand Customer Behaviour

Machine learning is increasingly helping marketers to understand and anticipate human behaviour, delivering value for the customer at the moment they want it. That said, there’s still a lot of guesswork and manual data processing involved in delivering personalised marketing campaigns.

When marketing campaigns are augmented with artificial intelligence, however, they are capable of much more. For example, a campaign can analyse whether a customer had responded well to a particular piece of creative, and determine the correct creative to show on the next engagement. Or a customer who had visited your bricks and mortar store recently might be shown creative related to the product they picked in-store using geo-targeting technology. Mapping customer identity with machine learning will enable marketers to be much more precise and personalised with their marketing efforts.

Improve Customer Experience

Artificial intelligence is already having a significant impact on customer experience. From Google Assistant to Amazon’s Alexa and Apple’s Siri, digital assistants have become a big part of our lives. That’s only going to increase.

Marketers are particularly well positioned to understand and anticipate how consumers are interacting with machines. Critical questions must be asked, such as how are these new technologies shaping consumer behaviour, and how does this impact my brand awareness or experience? What is the role of search and product discovery when voice is the primary internet access point? What role should automated chat-bots or digital assistants play in the traditional marketing funnel and how do we solve for this new paradigm? Where are consumers in the technology adoption curve, and how do I optimise to get the timing right?

Anytime there’s an opportunity to anticipate a customer need – from helping order movie tickets, to providing the answer to frequent customer service questions, there’s a chance to put AI to work. Voice recognition technologies, remember, all rely on AI to work. But understanding where to invest and why will mean the difference between hype chasing, and bottom line impact.

Cut Through the Hype

When a technology is as highly anticipated as AI, everyone wants to get in on the act. Some marketing technology vendors are also less than honest about what their AI powered tools can do, and no one solution offers a panacea. More often than not, people and business logic is just as critical to AI success. Just as technical literacy has always been an important part of a marketer’s skillset, AI literacy will become critical. It behoves all marketers to understand the basics of how machine learning algorithms work, the difference between supervised and unsupervised machine learning, and how to manage AI-assisted marketing and ad-tech tools. The closer marketers become to their in-house data science teams, and the easier the communication and collaboration, the greater the measurable impact will be.

Marketers today can harness AI to improve data processing, map the customer journey, optimise customer offers and improve the overall customer experience. One way to do this is to reach outside of the marketing organisation into data science and the supporting engineering and product groups of your business, as well as trusted technology partners and advisors. There are numerous guides to machine learning and artificial intelligence out there, many of which can be understood by those without a technical background. To help build a common language between marketers and data scientists, here is some reading to satisfy your hungry, and soon to be augmented, human minds. Good things will follow.

  • Machine Learning is Fun!, an excellent 15min detailed primer on machine learning from Adam Geitgey
  • Machine Intelligence Landscape (think AI ‘Lumascape’) by Shivon Zillis, founder of Bloomberg Beta, to understand the vendor and partner ecosystem better
  • If you really want to go deep on the foundations, Robbie Allen’s deeply curated list of AI/ML resources is excellent

And for the really brave, why not enroll in world renowned Andrew Ng’s Stanford online course on machine learning and get hands on yourself!