Lecture 3 Locally Weighted Logistic Regression | Stanford CS229 Machine Learning Autumn 2018
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Lessons List | 20
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![Lecture 4 Perceptron Generalized Linear Model | Stanford CS229 Machine Learning Autumn 2018](https://img.youtube.com/vi/iZTeva0WSTQ/hqdefault.jpg)
01:22:02
Lecture 4 Perceptron Generalized Linear Model | Stanford CS229 Machine Learning Autumn 2018
![Lecture 8 Data Splits Models Cross Validation | Stanford CS229 Machine Learning Autumn 2018](https://img.youtube.com/vi/rjbkWSTjHzM/hqdefault.jpg)
01:23:26
Lecture 8 Data Splits Models Cross Validation | Stanford CS229 Machine Learning Autumn 2018
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Course Description
Machine learning
Field of study
Machine learning is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Is machine learning hard?
Why is machine learning 'hard'? ... There is no doubt the science of advancing machine learning algorithms through research is difficult. It requires creativity, experimentation and tenacity. Machine learning remains a hard problem when implementing existing algorithms and models to work well for your new application.What is the goal of machine learning?
Machine Learning Defined
Its goal and usage is to build new and/or leverage existing algorithms to learn from data, in order to build generalizable models that give accurate predictions, or to find patterns, particularly with new and unseen similar data.What are the basics of machine learning?
Key Elements of Machine Learning
Every machine learning algorithm has three components: Representation: how to represent knowledge. Examples include decision trees, sets of rules, instances, graphical models, neural networks, support vector machines, model ensembles and others.
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