Skills you'll gain: Machine Learning, Probability & Statistics, General Statistics, Forecasting, Machine Learning Algorithms, Regression, Data Analysis, Deep Learning, Theoretical Computer Science, Artificial Neural Networks, Statistical Machine Learning, Algorithms, Business Analysis, Dimensionality Reduction, Exploratory Data Analysis, Feature Engineering, Computer Vision, Applied Machine Learning, Bayesian Statistics, NoSQL, Probability Distribution, Human Resources, Leadership Development, Leadership and Management, Data Management, Data Structures, Experiment, Linear Algebra, Mathematics, Computer Graphic Techniques, Computer Graphics, Computer Programming, Data Visualization, Natural Language Processing, Python Programming, Reinforcement Learning, Statistical Programming, Statistical Visualization, Algebra, Application Development, Basic Descriptive Statistics, Correlation And Dependence, Data Analysis Software, Estimation, SQL, Software Engineering, Statistical Analysis, Statistical Tests
Intermediate · Professional Certificate · 3-6 Months
Skills you'll gain: Machine Learning, Deep Learning, Tensorflow, Artificial Neural Networks, Computer Vision, Data Science, Computer Programming, Python Programming, Statistical Programming, General Statistics, Natural Language Processing, Probability & Statistics, Business Psychology, Entrepreneurship, Forecasting, Applied Machine Learning, Communication, Machine Learning Algorithms, Marketing, Calculus, Data Visualization, Mathematics, Programming Principles, Statistical Machine Learning, Computer Graphic Techniques, Computer Graphics, Machine Learning Software
Intermediate · Professional Certificate · 3-6 Months
Skills you'll gain: Machine Learning, Natural Language Processing, Statistical Programming, Python Programming, Artificial Neural Networks, Deep Learning, Machine Learning Algorithms, Data Science, Statistical Machine Learning, Human Computer Interaction, Probability & Statistics, User Experience, Algorithms, Bayesian Statistics, Communication, Computer Graphics, Computer Programming, Dimensionality Reduction, Experiment, General Statistics, Machine Learning Software, Markov Model, Mathematics, Operations Research, Regression, Research and Design, Strategy and Operations, Theoretical Computer Science
Intermediate · Specialization · 3-6 Months
Skills you'll gain: Mathematics, Algebra, Linear Algebra, Mathematical Theory & Analysis, Algorithms, Calculus, Machine Learning, Theoretical Computer Science, Artificial Neural Networks, Deep Learning, Machine Learning Algorithms, Probability & Statistics, Regression, Computer Programming, Differential Equations, Econometrics, General Statistics, Python Programming, Statistical Machine Learning, Statistical Programming, Statistical Analysis
Beginner · Specialization · 1-3 Months
Skills you'll gain: Machine Learning, Applied Machine Learning, DevOps, Python Programming, Statistical Programming, Tensorflow, Exploratory Data Analysis, Feature Engineering, Probability & Statistics, Cloud Computing, Data Management, Data Warehousing, Extract, Transform, Load, Computer Programming, Computer Vision, Deep Learning, Business Analysis, Change Management, Computer Networking, Data Analysis, Data Visualization, Estimation, General Statistics, Leadership and Management, Machine Learning Algorithms, Network Security, Security Engineering, Security Strategy, Statistical Visualization, Strategy and Operations
Advanced · Specialization · 3-6 Months
Skills you'll gain: Machine Learning, Theoretical Computer Science, Computational Thinking, Computer Programming, Applied Machine Learning, Entrepreneurship, Leadership and Management, Research and Design, Strategy and Operations, Machine Learning Algorithms, Reinforcement Learning, Accounting, Business Analysis, Business Communication, Business Psychology, Communication, Critical Thinking, Culture, Decision Making, Econometrics, Finance, General Accounting, General Statistics, Human Resources, Market Research, Marketing, Organizational Development, Probability & Statistics, Regulations and Compliance, Sales, Strategy, Algorithms
Beginner · Specialization · 3-6 Months
Skills you'll gain: Python Programming, Data Science, Data Analysis, Data Structures, Statistical Programming, Machine Learning, Data Mining, Regression, Machine Learning Algorithms, Data Visualization, General Statistics, Basic Descriptive Statistics, SQL, Applied Machine Learning, Statistical Analysis, Computer Programming Tools, Data Analysis Software, Machine Learning Software, Software Visualization, Databases, Programming Principles, Exploratory Data Analysis, Computer Programming, Statistical Visualization, Algebra, Data Management, Database Theory, Data Visualization Software, R Programming, Statistical Machine Learning, Statistical Tests, Deep Learning, Probability & Statistics, Extract, Transform, Load, Plot (Graphics), Devops Tools, SPSS, Estimation, Interactive Data Visualization, Algorithms, Database Application, Geovisualization, Reinforcement Learning, Theoretical Computer Science, Big Data, Business Analysis, Computational Logic, Correlation And Dependence, Database Administration, Econometrics, Entrepreneurship, Marketing, Mathematical Theory & Analysis, Mathematics, Spreadsheet Software, Storytelling, Supply Chain Systems, Supply Chain and Logistics, Writing
Beginner · Professional Certificate · 3-6 Months
Skills you'll gain: R Programming, Statistical Programming, General Statistics, Statistical Analysis, Data Analysis, Machine Learning, Probability & Statistics, Statistical Tests, Data Science, Machine Learning Software, Basic Descriptive Statistics, Bayesian Statistics, Correlation And Dependence, Econometrics, Estimation, Linear Algebra, Regression, Exploratory Data Analysis, Theoretical Computer Science, Data Visualization, Interactive Data Visualization, Natural Language Processing, Probability Distribution, Plot (Graphics), Machine Learning Algorithms, Algorithms, Applied Machine Learning, Business Analysis
Intermediate · Specialization · 3-6 Months
Skills you'll gain: Machine Learning, Artificial Neural Networks, Deep Learning, Machine Learning Algorithms, Statistical Programming, Python Programming, Machine Learning Software, Computer Vision, Computer Programming, Probability & Statistics, Applied Machine Learning
Intermediate · Specialization · 1-3 Months
If you're interested in artificial intelligence and want to learn more for free, check out Coursera's course selection. Specifically, AI and the Business Future of Work provides a great introduction to AI in a business setting. If you're more technically-minded, try Introduction to Embedded Machine Learning to gain insights on machine learning algorithms and learn how to create solutions that can run on almost any platform. If you want to take advantage of the cloud, Master Machine Learning on AWS is also available. If you want to learn more on the Ethical Implications of AI. Finally, if you're keen to learn about putting AI models into embedded devices, have a look at Computer Vision with Embedded Machine Learning to gain an understanding and start making computer vision projects from where ever you are.
For those new to Artificial Intelligence, an excellent starting point is Coursera's AI for Everyone course, which will introduce the fundamentals of machine learning. Another excellent course is Coursera's Introduction to AI, which explores supervised and unsupervised learning and natural language processing in detail. There is also the UoL Machine Learning for All course which provides an extensive introduction to the basic concepts and tools in machine learning. Additionally, Coursera's Artificial Intelligence Marketing course will teach you how to use AI to drive business value. Lastly, Coursera's Introduction to AI in the Data Center course provides an introduction to AI principles in the context of the data center environment.
If you are looking for an advanced artificial intelligence course, consider taking Introduction to Tensorflow or Machine Learning with Python. You could also learn about the data lifecycle in production with Machine Learning: Data Lifecycle in Production, develop exploratory data analysis skills with IBM Exploratory Data Analysis for Machine Learning, or dive into Python Project for AI Application Development.
Artificial intelligence (AI) is a fast-growing branch of computer science focused on enabling computers to perform a wide range of tasks that previously required human intelligence. Today, AI is used to power a wide range of tasks, such as image recognition, language translation, and prioritization of email or business workflows. So, if you have a smartphone, chances are you use software with AI capabilities every day.
AI is often discussed in tandem with the closely related concept of machine learning. Machine learning is the use of step-by-step processes called algorithms to allow computers to solve problems on their own - and, over time, get steadily better at doing so. Well-designed machine learning algorithms give computers the ability to solve a wide range of problems much more effectively and flexibly than if programmers had to provide detailed instructions for one specific use case.
While machine learning is used to create many simple AI applications, this approach typically requires massive, clearly-defined datasets to properly “train” the program. To create more sophisticated AI applications, an advanced type of machine learning called deep learning is used. Deep learning uses artificial neural networks that, as its name implies, are patterned after the human brain and do not require such structured datasets and human guidance to be successful. Instead, the AI application can be fed diverse, unstructured datasets and learn itself how to achieve a specified goal.
Even today’s most powerful deep learning approaches are not capable of mimicking the complexity and creativity of the human brain and its tens of billions of neurons. However, the field of artificial intelligence has made incredible strides in recent years, and is changing the way we live and work in ways that would have seemed outlandish a decade ago. Who knows what the next decade of progress in this exciting field will yield? Students learning skills in this area today may end up producing even more radical breakthroughs.
As artificial intelligence (AI) touches more and more areas of our daily lives, it is becoming useful to more and more career paths. Indeed, at least some background in this field is required for a growing number of jobs, and it can help give you a significant advantage over the competition in many others.
Naturally, AI and its subfields are in very high demand for popular computer science jobs. Data scientists rely on machine learning and deep learning skills in their daily work, applying various data mining techniques to both structured and unstructured big data in order to produce valuable insights for a wide variety of businesses. Skills in natural language processing (NLP) are needed to create useful chatbots for customer service as well as voice-activated assistants like Amazon’s Alexa. And advanced AI skills can put you on the cutting edge of computer programming, working on teams seeking to achieve ambitious goals like self-driving cars or autonomous robots.
A background in AI can help you in more and more jobs outside the realm of computer science, as well - it’s not much of an exaggeration to say that if a job requires human intelligence to do, artificial intelligence can help.
For example, a familiarity with machine learning can help business analysts understand and use sophisticated tools for predicting movements in the market - or develop these tools themselves. Doctors and other healthcare professionals are leveraging AI to assist with diagnosing illnesses, prescribing treatments, and analyzing medical data. Even creative professionals in visual arts and music can take advantage of AI tools to help them generate images and melodies.
Coursera offers online courses in an incredibly wide range of computer science topics, and artificial intelligence is no exception. If you’re a computer science student interested in this fast-growing field, online courses can give you an introduction to AI and machine learning, or help you hone your Python skills for data science. More advanced learners can dive deep, with courses and Specializations in AI engineering and deep learning. Even non-computer scientists can benefit from courses geared towards their field, such as the use of machine learning for trading and other business professionals.
Whatever your level of expertise and area of interest, online courses let you learn remotely on a flexible schedule and, typically, a lower cost than on-campus alternatives. And thanks to Coursera’s partnerships with top-ranked schools like Stanford University and Imperial College London, as well as industry leaders like IBM and Google Cloud, students can get all the advantages of online learning while still getting a high-quality education in this exciting field.
The skills or experience you may need to have before learning artificial intelligence (AI) includes having a solid knowledge of math, science, and computer science, specifically data science. You may want to have experience with advanced math, such as calculus and algebra, Bayesian algorithms, plus probability and statistics. In addition, a science background may be good to have for learning AI, including an understanding of physics, mechanics, cognitive learning theory, and language processing. It will also help to have a good command of computer science, including programming languages and tools such as Python, C++, and Java. Understanding the basic foundations of machine learning, deep learning, and neural networks may also be helpful to you before learning AI. If you already have some experience in software development, automotive manufacturing, and aerospace manufacturing fields, you may already have some necessary understanding of the way AI is applied in these industries.
The kind of people who are best suited for roles in AI are interested in highly scientific concepts and tools. People well suited to work in roles in AI feel comfortable experimenting with advanced technologies and concepts, such as machine learning, a part of AI that has given the world self-driving cars, for example. They also feel energized working with sophisticated pieces of software that can make decisions by analyzing data. In addition, the type of people well suited to work in roles in AI may want to learn to have the ability to build sophisticated pieces of equipment, such as robotics, which operate on internal software.
Learning artificial intelligence may be right for you if you plan on becoming an AI developer, machine learning engineer, data scientist, or research engineer or if you want your company to become better at using AI. In addition, learning AI may be beneficial if you are in the medical field, which AI is transforming when it comes to diagnosing, treating, and predicting outcomes. Learning AI may benefit you if you want to understand what AI realistically can and can't do and if you want to be able to spot opportunities to apply AI to your organization’s problems and know how to navigate the ethics of machine learning, along with other dimensions of AI.