AI and Machine Learning

AI and machine learning are driving innovation across industries. Learn more about generative AI, large language models, and more with these resources.

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10 Machine Learning Interview Questions (+ Tips to Answer Them)

Machine learning interviews give you the opportunity to showcase your skills, knowledge, and work. Read on to find some of the most common questions you can expect to be asked and find tips on how you can answer them with confidence.

April 1, 2022

Article

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3 Types of Machine Learning You Should Know

Machine learning is an exciting field and a subset of artificial intelligence. Use this guide to discover more about real-world applications, and the three types of machine learning you should know.

March 14, 2022

Article

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What Is a Machine Learning Engineer? (+ How to Get Started)

Machine learning engineers work with algorithms, data, and artificial intelligence. Learn about salary potential, job outlook, and steps to becoming a machine learning engineer.

February 22, 2022

Article

10 Machine Learning Interview Questions (+ Tips to Answer Them)

April 1, 2022

Article

3 Types of Machine Learning You Should Know

Machine learning is an exciting field and a subset of artificial intelligence. Use this guide to discover more about real-world applications, and the three types of machine learning you should know.

March 14, 2022

Article

AI and machine learning courses

IBM

Introduction to Artificial Intelligence (AI)

Skills you'll gain: Large Language Modeling, Artificial Intelligence, Generative AI, Data Ethics, Artificial Intelligence and Machine Learning (AI/ML), Applied Machine Learning, Deep Learning, Artificial Neural Networks, Governance, Prompt Engineering, Machine Learning, Automation, Digital Transformation, Business Transformation, Business Technologies, Ethical Standards And Conduct, Computer Vision, Emerging Technologies, Natural Language Processing

University of Pennsylvania

Artificial Intelligence Essentials

Skills you'll gain: Agentic systems, Artificial Intelligence, Algorithms, Python Programming, Theoretical Computer Science, Computer Science, Computer Programming

DeepLearning.AI

AI For Everyone

Skills you'll gain: Data Ethics, Market Opportunities, Artificial Intelligence and Machine Learning (AI/ML), Artificial Intelligence, Team Building, Machine Learning, Strategic Thinking, Data Science, Needs Assessment, Deep Learning, Business Ethics, Artificial Neural Networks, Engineering Management

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Deep Learning vs. Machine Learning: A Beginner’s Guide

Deep learning is machine learning, and machine learning is artificial intelligence. But how do they fit together (and how do you get started learning)?

June 4, 2021

Article

What Is a Machine Learning Engineer? (+ How to Get Started)

Machine learning engineers work with algorithms, data, and artificial intelligence. Learn about salary potential, job outlook, and steps to becoming a machine learning engineer.

February 22, 2022

Article

Deep Learning vs. Machine Learning: A Beginner’s Guide

Deep learning is machine learning, and machine learning is artificial intelligence. But how do they fit together (and how do you get started learning)?

June 4, 2021

Article

AI and machine learning are changing the way we work, creating new roles and shifting the way businesses innovate. If you're interested in learning about AI and machine learning, we've gathered free articles and resources on those and other data-centric topics, including:

Data analytics
Data basics
Data science
Generative AI and ChatGPT

Frequently asked questions (FAQ)

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.‎