What are machine learning methods?
Regression methods fall within the category of supervised ML. They help to predict or explain a particular numerical value based on a set of prior data, for example predicting the price of a property based on previous pricing data for similar properties.
The simplest method is linear regression where we use the mathematical equation of the line (y = m * x + b) to model a data set. We train a linear regression model with many data pairs (x, y) by calculating the position and slope of a line that minimizes the total distance between all of the data points and the line. In other words, we calculate the slope (m) and the y-intercept (b) for a line that best approximates the observations in the data.
Regression techniques run the gamut from simple (like linear regression) to complex (like regularized linear regression, polynomial regression, decision trees and random forest regressions, neural nets, among others). But don’t get bogged down: start by studying simple linear regression, master the techniques, and move on from there.
Another class of supervised ML, classification methods predict or explain a class value. For example, they can help predict whether or not an online customer will buy a product. The output can be yes or no: buyer or not buyer. But classification methods aren’t limited to two classes. For example, a classification method could help to assess whether a given image contains a car or a truck. In this case, the output will be 3 different values: 1) the image contains a car, 2) the image contains a truck, or 3) the image contains neither a car nor a truck.
The simplest classification algorithm is logistic regression — which makes it sounds like a regression method, but it’s not. Logistic regression estimates the probability of an occurrence of an event based on one or more inputs.
With clustering methods, we get into the category of unsupervised ML because their goal is to group or cluster observations that have similar characteristics. Clustering methods don’t use output information for training, but instead let the algorithm define the output. In clustering methods, we can only use visualizations to inspect the quality of the solution.
The most popular clustering method is K-Means, where “K” represents the number of clusters that the user chooses to create.
As you explore clustering, you’ll encounter very useful algorithms such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Mean Shift Clustering, Agglomerative Hierarchical Clustering, Expectation–Maximization Clustering using Gaussian Mixture Models, among others.
As the name suggests, we use dimensionality reduction to remove the least important information (sometime redundant columns) from a data set. In practice, I often see data sets with hundreds or even thousands of columns (also called features), so reducing the total number is vital. For instance, images can include thousands of pixels, not all of which matter to your analysis. Or when testing microchips within the manufacturing process, you might have thousands of measurements and tests applied to every chip, many of which provide redundant information. In these cases, you need dimensionality reduction algorithms to make the data set manageable.
The most popular dimensionality reduction method is Principal Component Analysis (PCA), which reduces the dimension of the feature space by finding new vectors that maximize the linear variation of the data. Another popular method is t-Stochastic Neighbor Embedding (t-SNE), which does non-linear dimensionality reduction. People typically use t-SNE for data visualization, but you can also use it for machine learning tasks like reducing the feature space and clustering, to mention just a few.
Imagine you’ve decided to build a bicycle because you are not feeling happy with the options available in stores and online. You might begin by finding the best of each part you need. Once you assemble all these great parts, the resulting bike will outshine all the other options.
Ensemble methods use this same idea of combining several predictive models (supervised ML) to get higher quality predictions than each of the models could provide on its own. For example, the Random Forest algorithms is an ensemble method that combines many Decision Trees trained with different samples of the data sets. As a result, the quality of the predictions of a Random Forest is higher than the quality of the predictions estimated with a single Decision Tree.
Think of ensemble methods as a way to reduce the variance and bias of a single machine learning model. The great majority of top winners of Kaggle competitions use ensemble methods of some kind. The most popular ensemble algorithms are Random Forest, XGBoost and LightGBM.
Neural Networks and Deep Learning
In contrast to linear and logistic regressions which are considered linear models, the objective of neural networks is to capture non-linear patterns in data by adding layers of parameters to the model.
In fact, the structure of neural networks is flexible enough to build our well-known linear and logistic regression. The term Deep learning comes from a neural net with many hidden layers (see next Figure) and encapsulates a wide variety of architectures.
It’s especially difficult to keep up with developments in deep learning, in part because the research and industry communities have doubled down on their deep learning efforts, spawning whole new methodologies every day.
In particular, deep learning techniques have been extremely successful in the areas of vision (image classification), text, audio and video. The most common software packages for deep learning are Tensorflow and PyTorch.
Transfer Learning refers to re-using part of a previously trained neural net and adapting it to a new but similar task. Specifically, once you train a neural net using data for a task, you can transfer a fraction of the trained layers and combine them with a few new layers that you can train using the data of the new task. By adding a few layers, the new neural net can learn and adapt quickly to the new task.
The main advantage of transfer learning is that you need less data to train the neural net, which is particularly important because training for deep learning algorithms is expensive in terms of both time and money (computational resources) — and of course it’s often very difficult to find enough labeled data for the training.
Transfer learning has become more and more popular and there are now many solid pre-trained models available for common deep learning tasks like image and text classification.
Reinforcement Learning is a machine learning method that helps an agent learn from experience. By recording actions and using a trial-and-error approach in a set environment, RL can maximize a cumulative reward. In our example, the mouse is the agent and the maze is the environment. The set of possible actions for the mouse are: move front, back, left or right. The reward is the cheese.
You can use RL when you have little to no historical data about a problem, because it doesn’t need information in advance (unlike traditional machine learning methods). In a RL framework, you learn from the data as you go. Not surprisingly, RL is especially successful with games, especially games of “perfect information” like chess and Go. With games, feedback from the agent and the environment comes quickly, allowing the model to learn fast. The downside of RL is that it can take a very long time to train if the problem is complex.
Natural Language Processing
Natural Language Processing (NLP) is not a machine learning method per se, but rather a widely used technique to prepare text for machine learning. Think of tons of text documents in a variety of formats (word, online blogs, ….). Most of these text documents will be full of typos, missing characters and other words that needed to be filtered out. At the moment, the most popular package for processing text is NLTK (Natural Language ToolKit), created by researchers at Stanford.
TFM and TFIDF are numerical representations of text documents that only consider frequency and weighted frequencies to represent text documents. By contrast, word embeddings can capture the context of a word in a document. With the word context, embeddings can quantify the similarity between words, which in turn allows us to do arithmetic with words.
Word representations allow finding similarities between words by computing the cosine similarity between the vector representation of two words. The cosine similarity measures the angle between two vectors.
We compute word embeddings using machine learning methods, but that’s often a pre-step to applying a machine learning algorithm on top. For instance, suppose we have access to the tweets of several thousand Twitter users. Also suppose that we know which of these Twitter users bought a house. To predict the probability of a new Twitter user buying a house, we can combine Word2Vec with a logistic regression.
You can train word embeddings yourself or get a pre-trained (transfer learning) set of word vectors. To download pre-trained word vectors in 157 different languages, take a look at FastText.