Machine learning is the science of getting computers to act without being explicitly programmed.
In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome.
Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress toward human-level AI.
In machine learning, there’s something called the “No Free Lunch” theorem. In a nutshell, it states that no one machine learning algorithm works best for every problem, and it’s especially relevant for supervised learning such as predictive modeling.
For example, you can’t say that neural networks are always better than decision trees or vice versa. There are many factors at play, such as the size and structure of your dataset.
Consequently it’s better to try many different algorithms for your problem, while using a hold-out “test set” of data to evaluate performance and select the winner.
Of course, the algorithms you try must be appropriate for your problem, which is where picking the right machine learning task comes in.
Machine learning algorithms are the engines of machine learning, meaning it is the algorithms that turn a data set into a model. Which kind of algorithm works best (supervised, unsupervised, classification, regression, etc.) depends on the kind of problem you’re solving, the computing resources available, and the nature of the data.
Ordinary programming algorithms tell the computer what to do in a straightforward way. For example, sorting algorithms turn unordered data into data ordered by some criteria, often the numeric or alphabetical order of one or more fields in the data.
Linear regression algorithms, for example, fit a straight line, or another function that is linear in its parameters such as a polynomial, to numeric data, typically by performing matrix inversions to minimize the squared error between the line and the data. Squared error is used as the metric because you don’t care whether the regression line is above or below the data points; you only care about the distance between the line and the points.
Want to know more about machine learning? Tonex offers two machine learning courses:
—Machine Learning Control Training, a 3-day course that covers the fundamentals of machine learning, a form and application of artificial intelligence (AI), and the fundamentals of control theory, an area of engineering related to control of continuously operating dynamical systems in engineered processes and machines.
—Machine Learning Training Bootcamp, a 3-day course for data scientists learning about complex theory, algorithms and coding libraries in a practical way with custom examples.
For more information, questions, comments, contact us.