Artificial Intelligence (AI) and Machine Learning (ML)
The most talked about and exciting development in the AI field is the development of Machine Learning, a data analytics technique that teaches computers to do what comes naturally to humans and animals: learn from experience.
The heart of machine learning is the algorithm. 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.
Algorithms 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.
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
- Spam detectors stop unwanted emails from reaching our inboxes
- Medical image analysis systems help doctors spot tumors they might have missed
- A key component in soon to arrive self-driving cars
Experts in this area are predicting many more applications involving machine learning are on the way, especially as big data keeps getting bigger, computing becomes more powerful and affordable, and as data scientists keep developing more capable algorithms.
AI and Machine Learning Training Courses by Tonex
Our three Machine Learning Training Courses covers a wide array of topics including:
- The Basics of Machine Learning
- Popular Machine Learning Methods
- Terminology and Principles
- Machine Learning Tools and Algorithms
- Applied Artificial Intelligence and Machine Learning
- Principles of Neural Networks
- Introduction to Deep Learning
Our Machine Learning Training Bootcamp is especially beneficial for busy professionals who want to stay current in their fields but have limited time to be away from the office.
Attendees learn, comprehend and master ideas on machine learning concepts, key principles, techniques including: supervised and unsupervised learning, mathematical and heuristic aspects, modeling to develop algorithms, prediction, linear regression, clustering, classification, and prediction.
Our Machine Learning for Control Training is a unique course that explores the fundamentals of control theory, an area of engineering related to control of continuously operating dynamical systems in engineered processes and machines.
Remember, Tonex courses can be tailored to your needs.
Contact us for more information, questions, comments.
AI and Machine Learning