Wrap Up
We will wrap up this course with a final note for our learners.
We'll cover the following
Course conclusion
Having completed this course, you should now have a strong understanding of the following:
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The primary principles of machine learning.
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How to use different machine learning models with sklearn.
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How to implement and use different machine learning models with Keras.
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How to implement linear regression, nonlinear regression, and a multilayer perceptron.
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Basic probability theory.
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Probabilistic regression and stochastic neural networks.
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The ideas of generative models such as Naive Bayes.
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Cyclic models and recurrent neural networks, which capture temporal aspects in modeling.
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Reinforcement learning, which captures the learning of agents and is a much more general setting of learning machines.
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The relationship between AI, the brain, and our society and the impact of machine learning on our society.
Congratulations!
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