Machine learning is a subfield of artificial intelligence (AI) and computer science that focuses on using data and algorithms to mimic the way people learn, progressively improving its accuracy. This way, Machine Learning is one of the most interesting methods in Computer Science these days, and it’s being applied behind the scenes in products and services we consume in everyday life.
In case you want to know what Machine Learning algorithms are used in different applications, or if you are a developer and you’re looking for a method to use for a problem you are trying to solve, keep reading below and use these steps as a guide.
Machine Learning can be divided into three different types of learning: Unsupervised Learning, Supervised Learning, and Semi-supervised Learning.
Unsupervised learning uses information data that is not labeled, that way the machine should work with no guidance according to patterns, similarities, and differences.
On the other hand, supervised learning has a presence of a “teacher”, who is in charge of training the machine by labeling the data to work with. Next, the machine receives some examples that allow it to produce a correct outcome.
But there’s a hybrid approach for these types of learning, this Semi-supervised learning works with both labeled and unlabeled data. This method uses a tiny data set of labeled data to train and label the rest of the data with corresponding predictions, finally giving a solution to the problem.
To begin, you need to know the number of dimensions you’re working with, it means the number of inputs in your problem (also known as features). If you’re working with a large dataset or many features, you can opt for a Dimension Reduction algorithm.
Unsupervised Learning: Dimension Reduction
A large number of dimensions in a data collection can have a significant influence on machine learning algorithms’ performance. The “curse of dimensionality” is a term used to describe the troubles large dimensionality might cause, for example, the “Distance Concentration” problem in Clustering, where the different data points will have the same value as the dimensionality of the data increases.
Techniques for minimizing the number of input variables in training data are referred to as “Dimension Reduction”.
Now you need to be familiar with the concept of Feature Extraction and Feature Selection to keep going. The process of translating raw data into numerical features that can be processed while keeping the information in the original data set is known as feature extraction. It produces better outcomes than applying machine learning to raw data directly.
It’s used for three known algorithms for dimensionality reduction including Principal Component Analysis, Singular Value Decomposition, and Linear Discriminant Analysis, but you need to know exactly which tool you want to use to find patterns or infer new information from the data.
If you’re not looking to combine the variables of your data, instead you want to remove unneeded features by just keeping the important ones, then you can use the Principal Component Analysis algorithm.
PCA (Principal Component Analysis)
It’s a mathematical algorithm for reducing the dimension of data sets to simplify the number of variables while retaining most of the information. This trade-off of accuracy for simplicity is extensively used to find patterns in large data sets.
In terms of linear connections, it has a wide range of applications when large amounts of data are present, such as media editing, statistical quality control, portfolio analysis, and in many applications such as face recognition and image compression.
Alternatively, if you want an algorithm that works by combining variables of the data you’re working with, a simple PCA may not be the best tool for you to use. Next, you can have a probabilistic model or a non-probabilistic one. Probabilistic data is data that involves a random selection and is preferred by most scientists for more accurate results. While non-probabilistic data doesn’t involve that randomness.
If you are working with non-probabilistic data, you should use the Singular Value Decomposition algorithm.
SVD (Singular Value Decomposition)
In the realm of machine learning, SVD allows data to be transformed into a space where categories can be easily distinguished. This algorithm decomposes a matrix into three different matrices. In image processing, for example, a reduced number of vectors are used to rebuild a picture that is quite close to the original.
Compared with the PCA algorithm, both can make a dimension reduction of the data. But while PCA skips the less significant components, the SVD just turns them into special data, represented as three different matrices, that are easier to manipulate and analyze.
When it comes to probabilistic approaches, it’s better to use the Linear Discriminant Analysis algorithm for more abstract problems.
LDA (Linear Discriminant Analysis)
Linear Discriminant Analysis (LDA) is a classification approach in which two or more groups have previously been identified, and fresh observations are categorized into one of them based on their features. It’s different from PCA since LDA discovers a feature subspace that optimizes group separability while the PCA ignores the class label and focuses on capturing the dataset’s highest variance direction.
This algorithm uses Bayes’ Theorem, a probabilistic theorem used to determine the likelihood of an occurrence based on its relationship to another event. It is frequently used in face recognition, customer identification, and medical fields to identify the patient’s disease status.
The next step is to select whether or not you want your algorithm to have responses, which means if you want to develop a predictive model based on labeled data to teach your machine. You may use the Clustering techniques if you’d rather use non-labeled data so your machine can work with no guidance and search for similarities.
On the other hand, the pro
