A machine learning algorithm (also known as a model) is a mathematical expression that represents data within the context of a problem. It is often a business problem. The goal is to transform data into insight. An example of this is an online retailer that wants to predict sales for the next quarter.
Top 10 Machine Learning Archetypes
They might use some machine-learning archetypes which can be used to forecast sales based upon past sales. Machine learning is a big part of the data science curriculum. These Top 10 Machine Learning Archetypes are worth checking out.
Regression
Regression methods are part of the machine-learning archetypes category. These methods are used to predict or explain a specific numerical value using a set of previous data. For example, they can be used to predict the price of a property by using pricing data from similar properties. Linear regression is the simplest way to model data.
To do this, we use the mathematical equation for the line (y =m*x+b). A linear regression model is created with multiple data pairs (x,y). We calculate the slope and position of the line that minimizes distance between the data points. We calculate the slope (m), and the yintercept (b), for the line that most closely approximates the data.
Classification
Another category of machine-learning archetypes is classification methods. These methods are used to predict or explain class values. They can be used to predict whether an online customer will purchase a product. The output could be either yes or no, buyer or not buyer. However, classification methods don’t have to be limited to just two classes.
A classification method can help determine if an image contains a truck or a car. The output of this method will have three different values: 1) it contains a vehicle, 2) it contains a truck or 3) it contains neither a vehicle nor a truck.
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Clustering
Clustering methods fall under the machine-learning archetypes category. They aim to cluster or group observations with similar characteristics. Clustering methods do not use output information to train, but let the algorithm determine the output. Clustering methods can only be used visualizations to assess the quality of the solution.
K-Means is the most common clustering method. K represents the number of clusters the user wants to create. (It is important to note that K-Means can be chosen in a variety of ways, including the elbow method.
Ensemble Methods
Imagine that you have decided to build your own bicycle. Start by deciding which parts are the most important. The result will be superior to all other bikes once you have assembled all of these parts. Ensemble methods combine several predictive models (supervisedML) to produce better-quality predictions than any one model could.
The Random Forest algorithm, for example, is an ensemble algorithm that combines multiple Decision Trees with different data sets. The quality of predictions from a Random Forest is better than those made with one Decision Tree.
Neural Network with one Hidden Layer
The structure of neural networks allows for flexibility to create our well-known logistic and linear regressions. Deep learning is a term that comes from a neural network with many layers (see next Figure), and can accommodate a variety of architectures.
Deep learning is a rapidly developing field that makes it difficult to keep up. This is partly because industry and research communities have increased their deep learning efforts, creating new methods every day.
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DeepLearning: Neural Network with Many Hidden Layers
Deep learning techniques need a lot data to achieve the best results. They also require lots of computing power because the method self-tunes many parameters in large architectures. Deep learning professionals need powerful computers that can be enhanced with GPUs (graphical processing unit).
Deep learning techniques are particularly effective in areas such as vision (image classification), audio, and video. Tensorflow, PyTorch are the most popular deep learning software packages.
Transfer Learning
Let’s say you are a data scientist in the retail industry. After months of training, you have a model that can classify images such as shirts, polos, and t-shirts. You now have to create a model that can classify images of dress types such as cargo, casual, formal, and jeans. You can transfer the knowledge from the first model to the second. Yes, you can, using Transfer Learning.
Transfer Learning is the process of using a portion of a previously-trained neural net to adapt it to a different task. You can transfer some of the layers you have trained using task data to train your neural net and then combine them with new layers you can train using that data. The new neural net will learn quickly and adapt to new tasks by adding layers.
Reinforcement Learning
Imagine a mouse trying to find hidden cheese pieces in a maze. The mouse will find the cheese more often it is exposed to the maze. Although the mouse may initially move in random ways, it will eventually learn which actions lead it closer to the cheese.
This is how Reinforcement Learning (RL), which we use to train a system, or a game, works for mice. RL, which is a machine learning method that allows agents to learn from their mistakes, can be described as a machine-learning technique. RL is a method that maximizes the cumulative reward by recording actions and using a trial and error approach in a controlled environment.
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Natural Language Processing
The vast majority of world’s knowledge and data is stored in human languages. Imagine being able to quickly read thousands of articles and blogs. Although computers can’t understand human language, we can train them for certain tasks. We can train our phones to automatically complete text messages and correct misspelled words. A machine can be taught to converse with a person.
Word Embedments
TFM and TFIDF represent text documents numerically by focusing on frequency and weighted frequencies. Word embeddings, on the other hand, can capture the context for a word within a document. Word contexts allow us to quantify the similarities between words. This allows us to perform arithmetic using words.
Word2Vec uses neural nets to map words from a corpus into a numerical vector. These vectors can be used to find synonyms, perform math operations with words, and represent text documents (by taking together all word vectors within a document).
