Today’s world of IT is increasingly embracing machine learning and artificial intelligence. As a result, more industries are waking up to the benefits of having machines and computers make decisions regarding repetitive jobs without involving human intervention, thereby freeing people up to do more critical tasks.
Different types of machine learning are available, but today we're focusing on patterns, or more specifically, machine learning models. This article defines machine models, their types and characteristics, and how to build them.
So, get ready to familiarize yourself with machine learning models. This article will boost your machine learning knowledge, which will come in handy if you’re applying for a machine learning-related job. In addition, you want to answer machine learning interview questions confidently.
What is a Machine Learning Model?
Before we explore machine learning models, let’s review machine learning’s basic definition. Machine learning is an offshoot of artificial intelligence, which analyzes data that automates analytical model building. Machine learning tells us that systems can, if trained, identify patterns, learn from data, and make decisions with little or no human intervention.
On the other hand, machine learning models are files trained to recognize particular pattern types. Models are also known as the resulting output of the training process and are considered the mathematical representation of real-world processes.
Data scientists train a model over a set of data, giving it the required algorithm to reason over and learn from the data. First, the training data must include the correct answer, also known as the “target attribute,” or just “target.” Next, the learning algorithm seeks out patterns in the training data that map the relevant data attributes to the correct answer, also known as the target. The algorithm then outputs a machine learning model designed to capture those patterns. This entire process is one of the most critical machine learning steps.
Ultimately, an effectively trained machine model can reason over data it’s never encountered before and make predictions related to that data, and all without human intervention. For instance, you can train a machine learning model to predict whether an email is legitimate or spam. The data scientist would give the machine learning the training data containing the label that ascertains whether an email is spam or legitimate. The machine model is then trained to predict whether future emails are spam or not.
Types of Machine Learning Models
Machine learning models come in many versions, just like there are plenty of different machine learning classifications. Of course, not everyone agrees on the exact number or breakdown of machine learning models, but we’re presenting two of the most common summaries.
For starters, some people split machine learning models into three types:
Data sets include their desired outputs or labels so that a function can calculate an error for any given prediction. The supervision part comes into play when a prediction is created, and an error is produced to change the function and learn the mapping. Supervised learning’s goal is to create a function that effectively generalizes over data it has never seen.
There are cases where a data set doesn’t have the desired output, so there’s no means of supervising the function. Instead, the process tries to segment the data set into “classes” so that each class has a segment of the data set with common features. Unsupervised learning aims to build a mapping function that classifies data based on features found within the data.
With reinforcement learning, the algorithm tries to learn actions for a given set of states that lead to a goal state. Thus, errors aren’t flagged after each example but rather on receiving a reinforcement signal, like reaching the goal state. This process closely resembles human learning, where feedback isn’t provided for every action, only when the situation calls for a reward.
Alternatively, we can break down machine learning models into five types. This approach gives a more specific and in-depth look at machine learning characteristics.
Classification predicts the class or type of an object according to a finite number of options. The classification output variable is always a category. For example, is this email spam or not?
Regression is a problem set where output variables can assume continuous values. For example, predicting the per barrel price of oil on the commodity market is a standard regression task. Regression models get further split into:
o Decision Trees
o Random Forests
o Linear Regression
This model involves gathering similar objects into groups. This process helps identify similar objects automatically without human intervention. Effective supervised machine learning models, including models that need to be trained with labeled or manually curated data, need homogeneous data, and clustering provides a smarter way to do it.
Sometimes, the number of possible variables in real-world data sets is too high, which leads to problems. Not all those countless variables even contribute significantly to the goal. Thus, we turn to dimensionality reduction, which preserves variances with a smaller number of variables.
This machine learning type involves neural networks. Neural networks are networks of mathematical equations. The network takes input variables, runs them through the equations, and produces output variables. The most significant deep learning models are:
o Boltzmann Machine
o Convolution Neural Networks
o Multi-layer perceptron
o Recurrent Neural Networks
How to Build a Machine Learning Model
There are seven steps to building a good machine learning model.
- Understand the business problem and what constitutes success. You need to understand a problem before you can fix it. This understanding involves working with the project owner and establishing the requirements and objectives. Then, figure out what parts of the business objective need a machine learning solution and how do you know when you’ve succeeded.
- Understand the data and identify it. Machine learning models rely on clean, plentiful training data to learn. Figure out what kinds of data you need and if it’s in good enough shape for the project. It would help establish where the data comes from, how much you need, and its condition. Furthermore, you must understand how and if the machine learning model will work with real-time data.
- Collect and prepare your data. Now that you know your data sources, you need to process the data into something suitable for machine learning training. This process includes collecting the data from its many sources, standardizing it, finding and replacing erroneous information, removing duplicate and extraneous information, and dividing the data into training, test, and validation sets.
- Train your model. Now comes the fun part. You must train your model to learn from the good quality data you’ve collected and processed. This step involves choosing a model technique, model training, selecting algorithms, and model optimization. Consult the machine learning model types mentioned above for your options.
- Evaluate the model’s performance and set up benchmarks. This step is analogous to the quality assurance aspect of application development. You must evaluate your model’s performance against the established requirements and metrics, which in turn determines how well you can expect it to work in the real world.
- Try out the model and make sure it performs as expected. This step is alternately known as operationalizing the model. Next, deploy the model in a way that you can continually measure and monitor its performance. Cloud environments are ideal for this. Next, develop benchmarks that you can use to measure future iterations of your model. Then, continuously iterate your model’s various aspects to improve its overall performance.
- Keep adjusting and iterating your model. Keep monitoring and improving your model. After all, technologies advance and change, business requirements evolve, and the real world occasionally throws a wrench into things. Any of these factors could potentially mean new requirements. Keep improving the model’s accuracy and performance. Think of your machine learning model as a mobile app. The application will always need tweaking, updating, and improving. The same thing applies to your machine learning model.
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