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Questions and Answers
Machine learning is the process of discovering patterns in data through model parameters.
True
The goal of machine learning is to create a model that is a poor approximation of the training data.
False
Supervised learning is a type of machine learning where the training data includes the desired outputs.
True
Unsupervised learning is a type of machine learning where the training data does not include the desired outputs.
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Clustering and dimension reduction are both types of supervised learning.
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Association, classification, and regression are all types of supervised learning.
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Ranking is a type of unsupervised learning.
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Reinforcement learning is a type of machine learning where the model learns from mistakes and rewards.
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Machine learning models are not affected by any kind of bias.
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Study Notes
Introduction to Machine Learning
- Machine Learning has broad applicability in daily life, including finance, entertainment, natural language processing, information retrieval, computer vision, robotics, healthcare, medicine, and biology.
- There is a close connection between theory and practice, and the field is open to new work, such as deep learning.
What Machine Learning Can Do
- Machine Learning enables "intelligent" machines to be "smarter" than humans.
- Examples of ML applications include IBM Watson Question Answering system, which beats Jeopardy champion Ken Jennings at Quiz bowl.
What is Learning?
- Learning is analogous to human learning, where you expect to "learn" a subject in a specific course.
- A good way to judge how well you do is by performing well on an exam that tests your ability to generalize.
Machine Learning Concepts
- Predicting the future based on the past is a key concept in Machine Learning.
- A program can be written to distinguish a picture of one person from another, or cancerous cells from normal cells, by providing examples and letting a classifier learn to distinguish.
Types of Machine Learning
- Supervised Learning: Training data includes desired outputs.
- Unsupervised Learning: Training data does not include desired outputs.
- Clustering, Dimensionality Reduction, Association, Classification, Regression, Ranking, and Reinforcement Learning are all types of Machine Learning.
Learning Process
- A trained model should be a good and useful approximation of the data.
- The learning process involves discovering patterns in data through model parameters.
- Each learning algorithm has a "bias" or inductive bias, which implies that some hypotheses are more probable than others.
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Description
Test your knowledge on the fundamentals of Machine Learning with this quiz covering the goals, intentions, and broad applicability of the field. Explore the close connection between theory and practice in various domains like finance, entertainment, healthcare, and more.
