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Understanding Machine Learning
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Gain an Introduction to Machine Learning Concepts
What's behind the machine learning hype? In this non-technical course, you’ll learn everything you’ve been too afraid to ask about machine learning. There’s no coding required.You will explore basic yet essential concepts to start your machine learning journey, using hands-on exercises to cement your knowledge. This includes developing an understanding beyond the jargon and learning how this exciting technology powers everything from self-driving cars to your personal Amazon shopping suggestions.
Explore the Machine Learning Basics
How does machine learning work, when can you use it, and what is the difference between AI and machine learning? This course covers all of these topics.You’ll start by unpacking what machine learning is, exploring its basic definition and its relation to data science and artificial intelligence. Then, you will familiarize yourself with its vocabulary and end with the machine learning workflow for building models.
We wrap up the course by digging deeper into deep learning. You will explore two common use cases for deep learning: computer vision and natural language processing (NLP), and acknowledge the limits and dangers of machine learning.
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Start Course for FreeWhat you'll learn
- Define machine learning and recognize its relationship to artificial intelligence and data science.
- Differentiate between supervised, unsupervised, and deep learning approaches and identify use cases for each.
- Identify the steps of the machine learning workflow, including feature engineering, training, testing, and tuning.
- Recognize model evaluation techniques such as confusion matrices and assess trade-offs between accuracy, precision, and recall.
- Evaluate the benefits and limitations of machine learning, including issues of explainability, bias, and data quality.
Prerequisites
There are no prerequisites for this courseWhat is Machine Learning?
Machine Learning Models
Deep Learning
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Enroll NowFAQs
What does this machine learning course cover?
This course provides a non-technical introduction to machine learning concepts. It begins with defining machine learning, its relation to data science and artificial intelligence, and understanding the basic terminology. It also delves into the machine learning workflow for building models, the different types of machine learning models, and methods for evaluating and improving these models. The course concludes with an introduction to deep learning, including its applications in computer vision and natural language processing.
Is there any coding required in this course?
No, this course does not require any coding. It is designed to be a conceptual and introductory course focusing on understanding the basics of machine learning.
Does this course cover the ethical aspects of machine learning?
Yes, the course concludes with a discussion about the limits and dangers of machine learning, which includes its ethical implications.
What are examples of machine learning?
There are many different real-world examples of machine learning. Among them are credit card fraud detection, spam filters, chatbots, disease prediction, customer churn rate prediction, and market basket analysis.
What is machine learning good for?
Machine learning is a set of data-based tools for generating insights and making predictions. Many sectors use machine learning to make more informed decisions, including banking, marketing, sales, healthcare, logistics, linguistics, education, insurance, and manufacturing.
Which language is best for machine learning?
Various programming languages are capable of machine learning, including Python, R, Java, JavaScript, C , Shell, and Go. Deciding the "best" machine learning language will depend on the purpose or goal of your project.
Is machine learning hard?
The difficulty of learning machine learning depends on your background and depth of study. However, our Understanding Machine Learning course simplifies these concepts, requiring no coding and starting with basic definitions. We aim to make machine learning accessible and achievable for all, regardless of their prior knowledge.
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