Machine Learning
Machine learning focuses on applications that learn from experience and improve their decision-making or predictive accuracy over time.
What is machine learning?
Machine learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time without being programmed to do so.
In data science, an algorithm is a sequence of statistical processing steps. In machine learning, algorithms are 'trained' to find patterns and features in massive amounts of data in order to make decisions and predictions based on new data. The better the algorithm, the more accurate the decisions and predictions will become as it processes more data.
Today, examples of machine learning are all around us. Digital assistants search the web and play music in response to our voice commands. Websites recommend products and movies and songs based on what we bought, watched, or listened to before. Robots vacuum our floors while we do . . . something better with our time. Spam detectors stop unwanted emails from reaching our inboxes. Medical image analysis systems help doctors spot tumors they might have missed. And the first self-driving cars are hitting the road.
We can expect more. As big data keeps getting bigger, as computing becomes more powerful and affordable, and as data scientists keep developing more capable algorithms, machine learning will drive greater and greater efficiency in our personal and work lives.
How machine learning works
There are four basic steps for building a machine learning application (or model). These are typically performed by data scientists working closely with the business professionals for whom the model is being developed.
Step 1: Select and prepare a training data set
Training data is a data set representative of the data the machine learning model will ingest to solve the problem it’s designed to solve. In some cases, the training data is labeled data—‘tagged’ to call out features and classifications the model will need to identify. Other data is unlabeled, and the model will need to extract those features and assign classifications on its own.
In either case, the training data needs to be properly prepared—randomized, de-duped, and checked for imbalances or biases that could impact the training. It should also be divided into two subsets: the training subset, which will be used to train the application, and the evaluation subset, used to test and refine it.
Step 2: Choose an algorithm to run on the training data set
Again, an algorithm is a set of statistical processing steps. The type of algorithm depends on the type (labeled or unlabeled) and amount of data in the training data set and on the type of problem to be solved.
Common types of machine learning algorithms for use with labeled data include the following:
Regression algorithms: Linear and logistic regression are examples of regression algorithms used to understand relationships in data. Linear regression is used to predict the value of a dependent variable based on the value of an independent variable. Logistic regression can be used when the dependent variable is binary in nature: A or B. For example, a linear regression algorithm could be trained to predict a salesperson’s annual sales (the dependent variable) based on its relationship to the salesperson’s education or years of experience (the independent variables.) Another type of regression algorithm called a support vector machine is useful when dependent variables are more difficult to classify.
Decision trees: Decision trees use classified data to make recommendations based on a set of decision rules. For example, a decision tree that recommends betting on a particular horse to win, place, or show could use data about the horse (e.g., age, winning percentage, pedigree) and apply rules to those factors to recommend an action or decision.
Instance-based algorithms: A good example of an instance-based algorithm is K-Nearest Neighbor or k-nn. It uses classification to estimate how likely a data point is to be a member of one group or another based on its proximity to other data points.
Algorithms for use with unlabeled data include the following:
Clustering algorithms: Think of clusters as groups. Clustering focuses on identifying groups of similar records and labeling the records according to the group to which they belong. This is done without prior knowledge about the groups and their characteristics. Types of clustering algorithms include the K-means, TwoStep, and Kohonen clustering.
Association algorithms: Association algorithms find patterns and relationships in data and identify frequent ‘if-then’ relationships called association rules. These are similar to the rules used in data mining.
Neural networks: A neural network is an algorithm that defines a layered network of calculations featuring an input layer, where data is ingested; at least one hidden layer, where calculations are performed make different conclusions about input; and an output layer. where each conclusion is assigned a probability. A deep neural network defines a network with multiple hidden layers, each of which successively refines the results of the previous layer. (For more, see the “Deep learning” section below.)
Step 3: Training the algorithm to create the model
Training the algorithm is an iterative process–it involves running variables through the algorithm, comparing the output with the results it should have produced, adjusting weights and biases within the algorithm that might yield a more accurate result, and running the variables again until the algorithm returns the correct result most of the time. The resulting trained, accurate algorithm is the machine learning model—an important distinction to note, because 'algorithm' and 'model' are incorrectly used interchangeably, even by machine learning mavens.
Step 4: Using and improving the model
The final step is to use the model with new data and, in the best case, for it to improve in accuracy and effectiveness over time. Where the new data comes from will depend on the problem being solved. For example, a machine learning model designed to identify spam will ingest email messages, whereas a machine learning model that drives a robot vacuum cleaner will ingest data resulting from real-world interaction with moved furniture or new objects in the room.
Machine learning methods
Machine learning methods (also called machine learning styles) fall into three primary categories.
For a deep dive into the differences between these approaches, check out "Supervised vs. Unsupervised Learning: What's the Difference?"
Supervised machine learning
Supervised machine learning trains itself on a labeled data set. That is, the data is labeled with information that the machine learning model is being built to determine and that may even be classified in ways the model is supposed to classify data. For example, a computer vision model designed to identify purebred German Shepherd dogs might be trained on a data set of various labeled dog images.
Supervised machine learning requires less training data than other machine learning methods and makes training easier because the results of the model can be compared to actual labeled results. But, properly labeled data is expensive to prepare, and there's the danger of overfitting, or creating a model so closely tied and biased to the training data that it doesn't handle variations in new data accurately.
Learn more about supervised learning.
Unsupervised machine learning
Unsupervised machine learning ingests unlabeled data—lots and lots of it—and uses algorithms to extract meaningful features needed to label, sort, and classify the data in real-time, without human intervention. Unsupervised learning is less about automating decisions and predictions, and more about identifying patterns and relationships in data that humans would miss. Take spam detection, for example—people generate more email than a team of data scientists could ever hope to label or classify in their lifetimes. An unsupervised learning algorithm can analyze huge volumes of emails and uncover the features and patterns that indicate spam (and keep getting better at flagging spam over time).
Learn more about unsupervised learning.
Semi-supervised learning
Semi-supervised learning offers a happy medium between supervised and unsupervised learning. During training, it uses a smaller labeled data set to guide classification and feature extraction from a larger, unlabeled data set. Semi-supervised learning can solve the problem of having not enough labeled data (or not being able to afford to label enough data) to train a supervised learning algorithm.
Reinforcement machine learning.
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