AI Blog Series
What is machine learning and how can it be used to improve business processes?
Introduction
Machine learning is a set of techniques for building systems
that can learn from data. It's one of the most exciting and
powerful applications of artificial intelligence, and it can be
used in a wide variety of ways. In this article, we'll explain
what machine learning is, how it works, and how you can use it
to improve your business processes.
Tim Davies
5 min read
Machine learning is one of the most exciting and powerful
applications of artificial intelligence.
Machine learning embodies a machine or computer program that
can learn from data, understand patterns and make
predictions based on those patterns.
Machine learning can be used to solve problems that are too complex for traditional programming. Machine learning draws upon data from previous cases to predict outcomes for new cases. For example, imagine a company has a goal of increasing sales by 25% over the next year but doesn't know exactly how it will achieve this goal—it might decide to use machine learning to figure out which products they should promote at what times and places in order to meet this target sales rate.
Machine learning can be used to solve problems that are too complex for traditional programming. Machine learning draws upon data from previous cases to predict outcomes for new cases. For example, imagine a company has a goal of increasing sales by 25% over the next year but doesn't know exactly how it will achieve this goal—it might decide to use machine learning to figure out which products they should promote at what times and places in order to meet this target sales rate.
Machine learning has many applications
Machine learning can be used in a wide variety of ways. One
example is improving business processes by using machine
learning to build predictive models. Machine learning can
also be used to improve the quality and efficiency of a
business process, as well as customer service.
The data that is collected by a business can be used to improve the company’s products and services. A company might use machine learning to analyze customer data, such as purchases or preferences. This information can then be used to make predictions about what customers will want and need in the future.
The applications of machine learning are wide-ranging, and it can be used in almost any industry.
The data that is collected by a business can be used to improve the company’s products and services. A company might use machine learning to analyze customer data, such as purchases or preferences. This information can then be used to make predictions about what customers will want and need in the future.
The applications of machine learning are wide-ranging, and it can be used in almost any industry.
Machine learning can find new patterns in data
Machine learning can find patterns in data that humans often
miss.
Take the example of Amazon, which uses machine learning algorithms to predict what customers are likely to buy next. By analyzing past customer purchases, it can decide what other products they may be interested in and recommend them. This has led to impressive growth for Amazon – its sales now exceed those of Walmart and Target combined.
It’s not just companies like Amazon that can benefit from using machine learning. Businesses across all industries can use this technology to improve their processes and make better decisions faster than before, resulting in increased efficiency and productivity at work.
Take the example of Amazon, which uses machine learning algorithms to predict what customers are likely to buy next. By analyzing past customer purchases, it can decide what other products they may be interested in and recommend them. This has led to impressive growth for Amazon – its sales now exceed those of Walmart and Target combined.
It’s not just companies like Amazon that can benefit from using machine learning. Businesses across all industries can use this technology to improve their processes and make better decisions faster than before, resulting in increased efficiency and productivity at work.
Machine learning algorithms predict outcomes based on data
from previous cases.
Data scientists use machine learning to build algorithms
that predict outcomes based on data from previous cases.
That means they can use historical data to help make
decisions about future events, and those predictions are
often more accurate than if a human made them by hand. For
example, a financial fraud team might want to know who might
be most likely to commit fraud in the future—so they could
use machine learning algorithms to find patterns among past
offenders and then target people who match those patterns
(like young adults with low credit scores).
This type of analysis is often used in business processes that involve repetitive tasks or large amounts of data; for instance, an insurance company might have hundreds of thousands of customers who've had claims filed against them over the years and wants to know which ones are likely candidates for filing another claim next month based on their previous claims history.
This type of analysis is often used in business processes that involve repetitive tasks or large amounts of data; for instance, an insurance company might have hundreds of thousands of customers who've had claims filed against them over the years and wants to know which ones are likely candidates for filing another claim next month based on their previous claims history.
ML models don't require explicit programming to arrive at
their results, they learn by example.
When you train a machine learning model, it learns by
example. In other words, you teach the model how to classify
data into different categories by feeding it examples of
what those classifications look like. Once you've done this,
these classifications become the basis for making
predictions about new data that might not have been seen
before.
This process is known as training the model. When we say that a model has been trained on a dataset, we mean that it has been fed all of (or at least most of) that dataset's inputs and outputs so that it can make predictions based on them later on (i.e., when asked).
Once a model has been trained on enough examples from its input domain—the set of potential inputs for which it will make predictions—it can be tested against new data from outside its input domain to see how well it performs overall: did our algorithm accurately predict whether or not someone was likely to buy something online? Did those who bought products online tend to come back again? And so forth...
This process is known as training the model. When we say that a model has been trained on a dataset, we mean that it has been fed all of (or at least most of) that dataset's inputs and outputs so that it can make predictions based on them later on (i.e., when asked).
Once a model has been trained on enough examples from its input domain—the set of potential inputs for which it will make predictions—it can be tested against new data from outside its input domain to see how well it performs overall: did our algorithm accurately predict whether or not someone was likely to buy something online? Did those who bought products online tend to come back again? And so forth...
Machine learning helps improve the effectiveness of algorithms
through experience.
In machine learning, an algorithm is trained to predict
future outcomes based on past results. For example, if you
want your algorithm to predict whether a customer will buy
something from you or not (the “outcome”), you would train
it by giving it sets of data that include examples of
customers who bought things and those who didn't. The
algorithm will then learn from this information and be able
to make predictions about whether other customers will buy
something or not based on what it has learned.
The more data that you give an algorithm, the better its predictive power becomes; this is why many companies are currently investing heavily in collecting large amounts of customer data so they can use machine learning models to understand their customers better than ever before.
The more data that you give an algorithm, the better its predictive power becomes; this is why many companies are currently investing heavily in collecting large amounts of customer data so they can use machine learning models to understand their customers better than ever before.
The best machine learning models are trained on high-quality
input data.
The quality of your machine learning model’s input data is
crucial for the model to deliver accurate results. Data
quality should be a priority for all organizations, not just
those looking to adopt machine learning. A recent study
found that companies with weak data governance practices are
more likely to fail in their use of AI and ML technologies.
The best way to ensure high-quality data is through effective data preparation, collection and storage processes. These steps help ensure that your organization will have access to clean, relevant information when it comes time for training or deployment. And by implementing these practices throughout the organization — from product development through operations — you can create an environment where data governance helps everyone make better business decisions based on real numbers rather than estimates or guesses.
The best way to ensure high-quality data is through effective data preparation, collection and storage processes. These steps help ensure that your organization will have access to clean, relevant information when it comes time for training or deployment. And by implementing these practices throughout the organization — from product development through operations — you can create an environment where data governance helps everyone make better business decisions based on real numbers rather than estimates or guesses.
Data scientists guide the training process with example inputs
and outputs and an optimization algorithm or loss function.
Data scientists guide the training process by providing the
model with example inputs and outputs and an optimization
algorithm or loss function. The data scientist then trains
the model by presenting it with many examples of inputs and
their corresponding outputs.
The model learns from these examples to predict outcomes on new, unseen data. It’s important that there are enough examples for this learning to happen effectively. For example, if you’re trying to train a computer vision model that can recognize objects in photos, you need to give it thousands of images tagged with labels specifying what is in each picture (a car or a dog). This process is called supervised learning because we “supervise” our machine by telling it what answer we want when we provide it with an input image — otherwise, it wouldn't know what answers were correct!
The model learns from these examples to predict outcomes on new, unseen data. It’s important that there are enough examples for this learning to happen effectively. For example, if you’re trying to train a computer vision model that can recognize objects in photos, you need to give it thousands of images tagged with labels specifying what is in each picture (a car or a dog). This process is called supervised learning because we “supervise” our machine by telling it what answer we want when we provide it with an input image — otherwise, it wouldn't know what answers were correct!
Supervised machine learning algorithms work with high-quality
labelled training data, Unsupervised machine learning
algorithms work with unlabelled data.
In supervised machine learning, you have a set of labelled
data that has been manually labelled by humans.
For example, you may have a database of responses to customer satisfaction surveys. For each survey response, you would have an answer provided by the respondent and a column indicating if the answer was positive or negative (e.g., "satisfied"). You can then train your algorithm on this set of labelled data as well as other signals such as location/demographics to predict whether new customers will be satisfied or disappointed with their purchase experience.
Supervised algorithms are most useful in problems where there is plenty of known information about the problem at hand and where some level of accuracy can be achieved by simply classifying things into categories (such as house types).
For example, you may have a database of responses to customer satisfaction surveys. For each survey response, you would have an answer provided by the respondent and a column indicating if the answer was positive or negative (e.g., "satisfied"). You can then train your algorithm on this set of labelled data as well as other signals such as location/demographics to predict whether new customers will be satisfied or disappointed with their purchase experience.
Supervised algorithms are most useful in problems where there is plenty of known information about the problem at hand and where some level of accuracy can be achieved by simply classifying things into categories (such as house types).
Machine learning can extract insights to improve your business
processes
Machine learning can be used to identify and extract
important insights from your business processes. These
insights can then be fed back into business processes to
help them run smoothly and efficiently, leading to higher
customer satisfaction and revenues.
For machine learning to generate actionable insights, it must first understand the intricacies of your business environment. Machine learning algorithms need an input that they can use as training data in order to learn what is relevant to your organization's processes. This training data should include past events or observations related to specific areas of interest (for example high-value customers or low-value customers). Training data could also include historical information about particular products or services (for example average sales volume per transaction), as well as information about competitors' offerings (if relevant).
For machine learning to generate actionable insights, it must first understand the intricacies of your business environment. Machine learning algorithms need an input that they can use as training data in order to learn what is relevant to your organization's processes. This training data should include past events or observations related to specific areas of interest (for example high-value customers or low-value customers). Training data could also include historical information about particular products or services (for example average sales volume per transaction), as well as information about competitors' offerings (if relevant).
Conclusion
Machine learning is a powerful tool for business. It can be
used in a wide variety of ways, from improving customer
experience to improving the efficiency of sales processes by
identifying patterns in data that humans often miss. It's
important for businesses to understand how machine learning
works before implementing it into their operations—this
article has given you some insight into what machine learning
is and how its application can benefit your company.
Tim Davies