Machine learning is considered to be a branch of both artificial intelligence (AI) and computer science. It uses algorithms to replicate the way that humans learn but can also analyse vast amounts of data in a short amount of time.
Machine learning algorithms are usually written to look for recurring themes (pattern recognition) and spot anomalies, which can help computers make predictions with more accuracy. This kind of predictive modelling can be for something as basic as a chatbot anticipating what your question may be about to something quite complex, like a self-driving car knowing when to make an emergency stop.
It was an IBM employee, Arthur Samuel, who is credited with creating the phrase “machine learning” in his 1959 research paper, “Some studies in machine learning using the game of checkers”. It’s amazing to think that machine learning models were being studied as early as 1959 given that computers now contribute to society in important areas as diverse as healthcare and fraud detection.
Is machine learning AI?
Machine learning represents just a section of AI capabilities. There are three major areas of interest that use AI – machine learning, deep learning, and artificial neural networks. Deep learning is a field within machine learning, and neural networks is a field within deep learning. Traditionally, machine learning is very structured and requires more human intervention in order for the machine to start learning via supervised learning algorithms. Training data is chosen by data scientists to help the machine determine the features it needs to look for within labelled datasets. Validation datasets are then used to ensure an unbiased evaluation of a model fit on the training data set. Lastly, test data sets are used to finalise the model fit.
Unsupervised learning also needs training data, but the data points are unlabelled. The machine begins by looking at unstructured or unlabelled data and becomes familiar with what it is looking for (for example, cat faces). This then starts to inform the algorithm, and in turn helps sort through new data as it comes in. Once the machine begins this feedback loop to refine information, it can more accurately identify images (computer vision) and even carry out natural language processing. It’s this kind of deep learning that also gives us features like speech recognition.
Currently, machines can tell whether what they’re listening to or reading was spoken or written by humans. The question is, could machines then write and speak in a way that is human? There have already been experiments to explore this, including a computer writing music as though it were Bach.
Semi-supervised learning is another learning technique that combines a small amount of labelled data within a large group of unlabelled data. This technique helps the machine to improve its learning accuracy.
As well as supervised and unsupervised learning (or a combination of the two), reinforcement learning is used to train a machine to make a sequence of decisions with many factors and variables involved, but no labelling. The machine learns by following a gaming model in which there are penalties for wrong decisions and rewards for correct decisions. This is the kind of learning carried out to provide the technology for self-driving cars.
Is clustering machine learning?
Clustering, also known as cluster analysis, is a form of unsupervised machine learning. This is when the machine is left to its own devices to discover what it perceives as natural grouping or clusters. Clustering is helpful in data analysis to learn more about the problem domain or understand arising patterns, for example, customer segmentation. In the past, segmentation was done manually and helped construct classification structures such as the phylogenetic tree, a tree diagram that shows how all species on earth are interconnected. From this example alone, we can see how what we now call big data could take years for humans to sort and compile. AI can manage this kind of data mining in a much quicker time frame and spot things that we may not, thereby helping us to understand the world around us. Real-world use cases include clustering DNA patterns in genetics studies, and finding anomalies in fraud detection.
Clusters can overlap, where data points belong to multiple clusters. This is called soft or fuzzy clustering. In other cases, the data points in clusters are exclusive – they can exist only in one cluster (also known as hard clustering). K-means clustering is an exclusive clustering method where data points are placed into various K groups. K is defined in the algorithm by the number of centroids (centre of a cluster) in a set, which it then uses to allocate each data point to the nearest cluster. The “means” in K-means refers to the average, which is worked out from the data in order to find the centroid. A larger K value is an indication of many, smaller groups, whereas a small K value shows larger, broader groups of data.
Other unsupervised machine learning methods include hierarchical clustering, probabilistic clustering (including the Gaussian Mixture Model), association rules, and dimensionality reduction.
Principal component analysis is an example of dimensionality reduction – reducing larger sets of variables in the input data without losing variance. It is also a useful method for the visualisation of high-dimensional data because it ranks principal components according to how much they contribute to patterns in the data. Although more data is generally helpful for more accurate results, it can lead to overfitting, which is when the machine starts picking up on noise or granular detail from its training data set.
The most common use of association rules is for recommendation engines on sites like Amazon, Netflix, LinkedIn, and Spotify to offer you products, films, jobs, or music similar to those that you have already browsed. The Apriori algorithm is the most commonly used for this function.
How does machine learning work?
Machine learning starts with an algorithm for predictive modelling, either self-learnt or programmed that leads to automation. Data science is the means through which we discover the problems that need solving and how that problem can be expressed through a readable algorithm. Supervised machine learning requires either classification or regression problems.
On a basic level, classification predicts a discrete class label and regression predicts a continuous quantity. There can be an overlap in the two in that a classification algorithm can also predict a continuous value. However, the continuous value will be in the form of a probability for a class label. We often see algorithms that can be utilised for both classification and regression with minor modification in deep neural networks.
Linear regression is when the output is predicted to be continuous with a constant slope. This can help predict values within a continuous range such as sales and price rather than trying to classify them into categories. Logistic regression can be confusing because it is actually used for classification problems. The algorithm is based on the concept of probability and helps with predictive analysis.
Support Vector Machines (SVM) is a fast and much-used algorithm that can be used for both classification and regression problems but is most commonly used in classification. The algorithm is favoured because it can analyse and class even when there is a limited amount of data available. It groups data into classes even when the classes are not immediately clear because it looks at the data three-dimensionally and uses a hyperplane rather than a line to separate it. SVMs can be used for functions like helping your mailbox to detect spam.
How to learn machine learning
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