Les cours Apprentissage automatique se concentrent sur la création de systèmes pour utiliser des ensembles de données volumineux et apprendre à partir de ces ensembles. Les thèmes d'étude incluent les algorithmes prédictifs, le traitement de langage naturel et la reconnaissance de formes statistiques....
Questions fréquentes sur Apprentissage automatique
Machine learning is a branch of artificial intelligence that seeks to build computer systems that can learn from data without human intervention. These powerful techniques rely on the creation of sophisticated analytical models that are “trained” to recognize patterns within a specific dataset before being unleashed to apply these patterns to more and more data, steadily improving performance without further guidance.
For example, machine learning is making increasingly accurate image recognition algorithms possible. Human programmers provide a relatively small set of images that are labeled as “cars” or “not cars,” for instance, and then expose the algorithms to vastly larger numbers of images to learn from. While the iterative algorithms typically used in machine learning aren’t new, the power of today’s computing systems have enabled this method of data analysis to become more effective more rapidly than ever.
Machine learning is in some ways a hybrid field, existing at the intersection of computer science, data science, and algorithms and mathematical theory. On the computer science side, machine learning engineers and other professionals in this field typically need strong software engineering skills, from fundamentals like confident programming and coding ability to big picture familiarity with system design principles.
A familiarity with data science concepts is also important, particularly skills in data modeling and evaluation to ensure that the algorithms perform well and become more, not less accurate over time. And, because machine learning relies heavily on algorithms as well as the statistics and probability principles that underlie them, a solid theoretical background in mathematics can also be invaluable.
Machine learning skills can open the door to a wide range of careers, as more and more companies seek to harness these techniques and artificial intelligence (AI) to automate a growing range of processes. Some companies may specifically hire for machine learning engineers, but machine learning skills can also be important for data scientists, data analysts, and data engineers.
There are more specialized roles available for machine learning experts, too. Many companies in the financial industry may employ business intelligence analysts and decision scientists who can leverage machine learning skills to automate systems for delivering market insights. And companies building Internet of Things (IoT) that rely on voice recognition or other human inputs may employ natural language processing engineers or human-centered machine learning designers.
If you want to develop your machine learning skills in the context of a degree program, you can do that online too! Coursera currently offers computer science and data science degrees from top-ranked colleges like University of Illinois, Imperial College London, University of Michigan, University of Colorado Boulder, and University of Pennsylvania, all of which offer opportunities to learn about machine learning at top-ranked universities from anywhere in the world.
Before starting to learn machine learning, the skills and experience you might want to already have may include an understanding of applied mathematics, statistics, data modeling, and computer science fundamentals. You may also want to have skills in programming languages used in machine learning, like Python and others. These skills will be worthy to have to help you learn how computer algorithms use statistics to find patterns in massive amounts of data, including numbers, words, images, videos, and more. If you already know how recommendation systems work, like the ones used on your streaming channels and your social media feeds, then you might already be understanding machine learning.
The kind of people that are best suited for work that involves machine learning are data scientists, data engineers, mathematicians, and statisticians. These professionals are well-paid knowledge workers, using their number-crunching skills to find patterns in large amounts of data in order to better automate certain computer processes. Machine learning professionals may work at the leading edge of technology, and machine learning would be a strong area to grow.
You might know if machine learning is right for you if you are passionate about how computer systems are rapidly advancing in using data to detect the preferences and needs of users. You might have a good grasp of how data works, and how it’s used in the computer customer experience. Having strong problem-solving skills, good analytical thinking, and critical insights can also help you know if moving toward machine learning is right for you.
The topics you might want to study that are related to machine learning include neural networks, logistic regression, algorithms, data quality, supervised and unsupervised learning, deep learning, and linear regressions principles. These are complex areas to undertake study in. Having a sound background in mathematics and computer science can help you better understand this fascinating era in which computers can learn to make decisions or predictions using accrued data.
Le contenu de cette FAQ a été mis à disposition à des fins d'information uniquement. Il est conseillé aux étudiants d'effectuer des recherches supplémentaires afin de s'assurer que les cours et autres qualifications suivis correspondent à leurs objectifs personnels, professionnels et financiers.