10 machine learning archetypes for learnings for data science students
Amachine learning algorithm, also called a model, is a mathematical expression that represents data in the context of a problem, often a business problem. The aim is to go from data to insight. For example, if an online retailer wants to anticipate sales for the next quarter, they might use some machine learning archetypes that predict those sales based on past sales and other relevant data. Learnings for data science students have a wide range and machine learning covers a major part of it. Here are 10 machine learning archetypes to check out.
Regression
Regression methods fall within the category of machine learning archetypes. 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.
Classification
Another class of machine learning archetypes, 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.
Clustering
With clustering methods, we get into the category of machine learning archetypes 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. (Note that there are various techniques for choosing the value of K, such as the elbow method.)
Ensemble Methods
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 algorithm 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.
Neural Network with One Hidden Layer
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.
Deep Learning: Neural Network with Many Hidden Layers
For the best performance, deep learning techniques require a lot of data — and a lot of compute power since the method is self-tuning many parameters within huge architectures. It quickly becomes clear why deep learning practitioners need very powerful computers enhanced with GPUs (graphical processing units).
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
Let’s pretend that you’re a data scientist working in the retail industry. You’ve spent months training a high-quality model to classify images as shirts, t-shirts, and polos. Your new task is to build a similar model to classify images of dresses as jeans, cargo, casual, and dress pants. Can you transfer the knowledge built into the first model and apply it to the second model? Yes, you can, using Transfer Learning. 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.
Reinforcement Learning
Imagine a mouse in a maze trying to find hidden pieces of cheese. The more times we expose the mouse to the maze, the better it gets at finding the cheese. At first, the mouse might move randomly, but after some time, the mouse’s experience helps it realize which actions bring it closer to the cheese. The process for the mouse mirrors what we do with Reinforcement Learning (RL) to train a system or a game. Generally speaking, RL 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.
Natural Language Processing
A huge percentage of the world’s data and knowledge is in some form of human language. Can you imagine being able to read and comprehend thousands of books, articles, and blogs in seconds? Computers can’t yet fully understand the human text but we can train them to do certain tasks. For example, we can train our phones to autocomplete our text messages or to correct misspelled words. We can even teach a machine to have a simple conversation with a human.
Word Embeddings
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. Word2Vec is a method based on neural nets that map words in a corpus to a numerical vector. We can then use these vectors to find synonyms, perform arithmetic operations with words, or represent text documents (by taking the mean of all the word vectors in a document).
Source: analyticsinsight.net
