What are machine learning algorithms?

Learning to make predictions

Although ML algorithms can learn what functions are, their primary purpose is to predict how data is affected when variables are added to the equation. These estimations will have errors, as machines can only use the data available to them.

Machine learning functions are optimized to reduce the degree of error in these predictions. Over time, algorithms will become better at reducing the margin of error.

Variety in the functions

The various machine learning algorithms make different assumptions about functions and how to represent them. That’s why you should apply different algorithms to the ML problem until you find the one that yields the most accurate result.

How to create machine learning algorithms from scratch

Creating a functional machine learning algorithm requires time and effort, but it’s rewarding once you master the fine details associated with writing your own algorithm.

If you want to create machine learning algorithms from scratch, we recommend using an existing neural network as your foundation and following these simple steps:

Understand the algorithm

First, you’ll need to decide which type of machine learning algorithm you want to create. Once you’ve chosen an ML algorithm type, familiarize yourself with its structure, capabilities, and limitations.

Research as much as possible

Next, it’s time to hit the books (or YouTube). You’ll find a wealth of information both online and offline.

Tangible resources like textbooks are great for providing in-depth mathematical details about algorithms. But if you want some easy-to-digest content and practical examples, we recommend watching tutorial videos or checking out blog posts like this one.

Turn a big problem into smaller ones

Creating your own machine learning algorithms can seem daunting. But you can make your task more manageable by first breaking it down into smaller parts.

So, rather than trying to build the entire algorithm all at once, try tackling it one piece at a time. For example, before you train it on a dataset, make sure the algorithm can read it. This could mean teaching it how to handle null values or categorical data. An incremental approach makes the process more digestible, and it can also prevent lengthy debugging further on down the line.

Start simple

Choose a small, simple dataset to run in your algorithm. Doing so will make it easy to manually input it into your algorithmic code. We suggest using a NAND gate, a common logic gate that developers use when creating digital devices.

Put the algorithm to the test

After running some simple tests on your algorithm and optimizing it, supply it with a larger, real-world dataset. Go back and make adjustments as needed to reduce the margin of error. If possible, use data that’s already been analyzed by an established ML system so you can evaluate the accuracy of your new algorithm.

Write it all down

Finally, write down everything you’ve learnt during this process. Doing so will help you better understand what worked and how you can streamline the process in the future.

Advantages and disadvantages

There are many pros and cons to ML algorithms. Let’s explore some of these advantages and disadvantages so you can better understand when and how to use machine learning.

Key advantages

1. Little or no human interaction is needed once the algorithm is written. After your machine learning algorithms go live, they can improve their analytical capabilities and make predictions without constant input from your team.
2. Trends and patterns are easy to spot. ML algorithms are great at pinpointing patterns and trends in consumer behavior, such as ecommerce shopping habits, without requiring human effort.
3. The algorithms continuously improve as they run. Over time, machine learning algorithms become more efficient and accurate by referencing historical data.
4. They’re capable of handling complex data. ML algorithms can handle multidimensional data, meaning they can work with huge datasets that include a wide range of variables.
5. You can apply them to many services. ML algorithms can be used in a broad spectrum of industries, from healthcare to ecommerce.

Potential disadvantages

1. Large amounts of data are needed to train and learn. Machine learning algorithms require massive amounts of high-quality, unbiased data to learn and improve.
2. Lots of time is required to run over a long period. You’ll have to give your ML algorithms time to ingest, analyze, and learn from your data. You’ll also have to devote extensive computing resources to the technology for it to become effective.
3. Results can be difficult to interpret. If you want your ML algorithm to generate digestible results, you must choose the appropriate formula for your intended use case.
4. Machine learning can be susceptible to errors. Although ML algorithms run autonomously, they’re highly susceptible to errors, especially when you use the wrong type of algorithm or provide it with poor-quality data.

By being cognizant of the shortcomings of machine learning algorithms, you’ll know how to use this revolutionary technology properly to make actionable predictions from your business data.

What types of machine learning algorithms exist?

Supervised learning

With supervised learning, you provide the machine learning algorithm with a labelled dataset that includes inputs and outputs. The algorithm is tasked with finding a function or method for using the provided inputs to achieve the desired outputs.

After each round of testing, you can correct known errors so the algorithm can learn from its mistakes.

Semi-supervised learning

During semi-supervised learning, you’ll provide the algorithm with both labelled and unlabeled information. Labelled data includes tags to help the algorithm understand the data, whereas unlabeled data doesn’t include any tags.

By mixing unlabeled and labelled data, you teach the algorithm to analyze unstructured information.

Reinforcement learning

Reinforcement learning uses regimented processes to expedite the learning process. You must provide your machine learning algorithm with a detailed set of parameters, actions, and extensible data notation (EDN) values.

This approach requires the greatest amount of human input, but it’s highly effective at teaching your algorithm to use trial-and-error analytics processes to reduce errors.

Unsupervised learning

In unsupervised learning, you feed the algorithm unlabeled data and allow it to freely determine relationships and correlations between datasets. The machine learning algorithm is left to sift through huge datasets independently.

What are machine learning evolutionary algorithms?

Machine learning evolutionary algorithms are computer applications that mimic the behavior of living organisms in order to solve complex problems. They rely on mechanisms like mutation, recombination, and reproduction.

Whereas traditional ML algorithms rely on trial and error, evolutionary algorithms use a process like natural selection.

After each round of analysis, evolutionary algorithms eliminate “weak” solutions from their list of predictions and keep “strong” ones. This process aims to identify which actions will most likely yield the desired outputs.

Machine learning evolutionary algorithms provide significant business benefits, including:

• Increased flexibility to take on just about any problem
• Better optimization to examine every possible action
• Unlimited solutions to numerous potential problems

Evolutionary algorithms are the best tool for examining complex datasets with many variables. Learn more about machine learning and how it differs from artificial intelligence.

Popular machine learning algorithms available

There are many different machine learning algorithms, but here are some of the most popular:

• Linear regression. The linear regression statistical model enables you to predict the link between dependent and independent variables. For instance, you may use linear regression models to estimate how increasing your pay-per-click marketing budget will impact leads, engagement, or ROI (return on investment).
• Logistic regression. This statistical model is used to estimate binary values like 0 or 1. Logistic regression can help you predict the likelihood that a given event will occur.
• K-nearest neighbors (KNN) algorithm. The KNN algorithm can be used to address either regression or classification problems. When used for classification, the KNN algorithm will classify new data points by “taking a vote” of its neighbors. The new case will be assigned to the group with which it has more in common.
• Decision tree algorithm. This is one of the most widely used algorithms, as it can help data scientists classify problems. The algorithm divides the dataset into several homogeneous groups based on key independent variables or attributes. The algorithm then charts these groups into a graph that resembles an inverted tree.

Want to learn more about machine learning algorithms and how this robust technology is used in the Adobe Real-Time Customer Data Platform? Watch the overview video or take a product tour.

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