Many people equate machine learning (ML) to AI, whether they recognize it or not. ML is one of the most exciting and promising subsets in this field, and it all hinges on machine learning model training.
If you want an algorithm to answer questions or work autonomously, you must first teach it to recognize patterns. That process is called training and is arguably the most important step in the machine-learning journey. Training lays the foundation for ML models’ future use cases and is where their success or failure stems from. Here’s a closer look at how it works.
The Basics of Machine Learning Model Training
Machine learning training starts with data mining in many cases. This is the resource with which you’ll teach your algorithm, so reliable training begins with gathering relevant, accurate information. Data scientists will often start with data sets they’re familiar with to help spot inaccuracies, preventing problems down the line. Remember, your ML model can only be as effective as its information is accurate and clean.
Next, data scientists choose a model that fits the pattern recognition they want. These vary in complexity, but it all boils down to finding similarities and differences in data sets. You’ll give the model some rules for identifying different patterns or types of information, then adjust it until it can accurately recognize these trends.
From there, the training process is a long series of trial and error. You’ll give the algorithm some more data, see how it interprets it, then adjust it as necessary to make it more accurate. As the process continues, the model should get increasingly reliable and handle more complex problems.
ML Training Techniques
The basics of ML training remain largely the same between methods, but specific approaches vary widely. Here are a few of the most common machine learning training techniques you’ll see in use today.
1. Supervised Learning
Most ML techniques fall into two major categories: supervised or unsupervised learning. Supervised approaches use labeled datasets to improve their accuracy. Labeled inputs and outputs provide a baseline for the model to measure its performance against, helping it learn over time.
Supervised learning generally serves one of two tasks: classification, which puts data into categories, or regression, which analyzes the relationships between different variables, often making predictions from this insight. In both cases, supervised models offer high accuracy but involve a lot of effort from data scientists to label them.
2. Unsupervised Learning
By contrast, unsupervised approaches to machine learning don’t use labeled data. As a result, they require minimal human interference, hence the “unsupervised” title. That can be helpful given the growing shortage of data scientists, but because they work differently, these models are better suited to other tasks.
Supervised ML models are good at acting on relationships in a dataset, while unsupervised ones reveal what those connections are. Unsupervised is the way to go if you need to train a model to uncover insight from data, like in anomaly detection or process optimization.
3. Distributed Training
Distributed training is a more specific technique in ML model training. It can be either supervised or unsupervised and divides workloads across multiple processors to speed the process. Instead of running one data set at a time through a model, this approach uses distributed computing to process multiple data sets simultaneously.
Because it runs more at once, distributed training can significantly shorten the time it takes to train a model. That speed also lets you create more accurate algorithms, as you can do more to refine them within the same time frame.
4. Multitask Learning
Multitask learning is another type of ML training that does multiple things simultaneously. In these techniques, you teach a model to do several related tasks at once instead of new things one by one. The idea is that this grouped approach produces better results than any single task by itself.
Multitask learning is helpful when you have two problems with crossover between their data sets. If one has less labeled information than the other, what the model learns from the more well-rounded set can help it understand the smaller one. You’ll often see these techniques in natural language processing (NLP) algorithms.
Transfer learning is similar but takes a more linear approach. This technique teaches a model one task, then uses that as a baseline to start learning something related. As a result, the algorithm can get increasingly accurate over time and manage more complex problems.
Many deep learning algorithms use transfer learning because it’s a good way to build to increasingly challenging, complicated tasks. Considering how deep learning accounts for 40% of the annual value of all data analytics, it’s worth knowing how these models come about.
Machine Learning Model Training Is a Wide Field
These five techniques are just a sample of how you can train a machine-learning model. The basic principles remain the same across different approaches, but ML model training is a vast and varied area. New learning methods will emerge as the technology improves, taking this field even further.