We are surrounded by Machine Learning.
Every hour, Google uses it to provide millions of search results. It assists Facebook in predicting your next love interest. Elon Musk’s Tesla also uses it to create self-driving cars. However, for those who are new to the field, Machine Learning can be intimidating.
In this blog, we’ll explain what Machine Learning is and how it works.
You can either watch the 5-minute video embedded below, or scroll down to continue reading.
How Do You Create a Machine Learning Model?
Machine Learning (ML) is a type of data analysis that is considered a subset of Artificial Intelligence.
We build predictive models based on computer algorithms that contain data during the Machine Learning process. Creating an effective Machine Learning model can be compared to parenting.
In this analogy, the ML model is the child, and the data scientist working on it is the parent. Their primary goal is to raise a problem-solving child. To become an excellent problem solver, the child must first learn how to deal with his or her surroundings. There are many unknowns at first, but their logic will improve over time. The child will become a brilliant problem solver if given enough life experience and useful lessons.
This is exactly what we want from an ML model: problem-solving abilities!
It’s all about learning from mistakes. Machine Learning does not use a pre-programmed equation. Instead, the algorithm learns from experience, which is provided in the form of training data. The more data you have of higher quality, the better the model’s results will be.
A child (or adult) may be talented, but lacks experience, especially if they haven’t practised enough. In such cases, they are likely to be outperformed by someone with average talent who continues to learn and improve themselves.
The same is true of Machine Learning models. The more training data you have, the better the output. In most cases, a sophisticated Machine Learning algorithm trained with a small amount of data will perform worse than a fairly simple algorithm trained with a large amount of data.
What are The Three Main Types of Machine Learning?
There are three main types of Machine Learning:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
Let’s briefly explain each of them.
- Supervised learning
Labeled data is used in supervised learning. In this case, the parent is very active, pointing out to the child whether a certain type of behaviour is ‘good’ or ‘bad.’ In fact, the parent provides numerous labelled examples. Based on this prior knowledge, the child attempts to produce a behavioural pattern that corresponds to the parent’s initial guidelines.
- Unsupervised learning
Unsupervised learning, on the other hand, is used when there is no labelled data. Our experiences are unlabeled; they are not classified as “good” or “bad.” The parent allows the child to explore the world on his or her own. They will be unable to recognise and categorise experiences as ‘good’ or ‘bad’ without initial guidance. However, that is not the goal. The parent hopes that by using this technique, the child will eventually be able to distinguish and point out different types of behaviour based on their similarities and differences.
- Reinforcement Learning
Reinforcement learning is the third type of Machine Learning. This type is feedback-driven. When a parent observes a positive behaviour in their child, they reward them. Similarly, punishment is used to discourage bad behaviour.
Machine Learning models, like parenting styles, can be tweaked over time if the data scientist believes that changing some of the model’s parameters will result in more accurate results. As a result, the art of the data scientist and Machine Learning engineer professions is frequently found in fine-tuning an already well-performing model. In some cases, a 0.1% increase in accuracy could be significant, especially when the ML model is used in areas such as healthcare, fraud prevention, and self-driving cars.
We can distinguish between traditional Machine Learning methods and Deep Learning in terms of the complexity of a model that a data scientist can create.
Which Traditional Machine Learning Techniques Are the Most Popular?
Traditional supervised Machine Learning techniques that are widely used include:
- Regression
- Logistic regression
- Time series
- Support vector machines
- Decision trees
These methods enable us to forecast future values or classify data based on predefined categories.
Traditional unsupervised ML techniques, on the other hand, such as K-means clustering, are primarily used for clustering items in input data and analysing patterns in these clusters.
In some cases, data scientists use Principal component analysis (PCA) to reduce dimensionality – that is, to determine which variables in a dataset make the most significant contribution.
What Exactly Is Deep Learning?
If Machine Learning is considered a subset of AI, then Deep Learning is a subset of ML.
Deep Learning (DL) was inspired by research into how the human brain works. It is based on a multi-layered structure known as a neural network. Each of these layers can be thought of as a classic ML mini-model, and they all learn together.
When a neural network has more than three layers, we can say it performs Deep Learning. A neural network’s complexity increases as the number of layers increases. And the greater its learning capacity. The outputs of each layer in a neural network serve as inputs for the next layer.
Deep Learning is the best solution for tasks such as:
- Image recognition and video recognition
- Speech classification and speech recognition
- Natural Language Processing (NLP)
Basically, everything cool about AI that is presented at innovation summits.
How to Choose Between Machine Learning and Deep Learning?
The short answer is: it depends on how complex their data is.
When dealing with simpler data, the traditional approach suffices, whereas complex data will almost certainly necessitate the use of neural networks. In almost every case, Deep Learning outperforms traditional Machine Learning methods in terms of precision. However, it necessitates a higher level of sophistication, is more difficult to interpret, and isn’t as efficient in terms of the time required to prepare the model as traditional methods.
The important thing to remember is that Machine Learning is a tool that, when used ethically, has the potential to empower people. It enables us to reduce our workload at scale and is invaluable in situations where we must deal with a large amount of incoming data and make a large number of micro decisions on a regular basis.
Next Steps in Machine Learning
You can begin learning how to apply Machine Learning now that you have a basic understanding of what it is.
Are you ready to take the plunge? Contact Us Now.
