After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Course Description
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.
Feels like what you want to learn?
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 course
Curriculum
Course outline
1
What is Machine Learning?
In this chapter, we'll define machine learning and its relation to data science and artificial intelligence. Then, we'll unpack important machine learning jargon and end with the machine learning workflow for building models.
- What is machine learning?50 XP
- Generating movie recommendations50 XP
- AI, data science, and machine learning walk into a bar...50 XP
- What's true about machine learning?100 XP
- Machine learning concepts50 XP
- Machine learning lingo100 XP
- Supervised vs unsupervised50 XP
- Machine learning workflow50 XP
- Steps for building a model100 XP
- A true step50 XP
2
Machine Learning Models
Now that you know the basics of machine learning, let's dive a little bit deeper. At the end of this chapter, you will know the different types of machine learning, as well as how to evaluate and improve your models.
3
Deep Learning
In this chapter, we'll unpack deep learning beginning with neural networks. Next, we'll take a closer look at two common use-cases for deep learning: computer vision and natural language processing. We'll wrap up the course discussing the limits and dangers of machine learning.
R
Understanding Machine Learning
Course
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