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:

The primary principles of machine learning.

How to use different machine learning models with sklearn.

How to implement and use different machine learning models with Keras.

How to implement linear regression, nonlinear regression, and a multilayer perceptron.

Basic probability theory.

Probabilistic regression and stochastic neural networks.

The ideas of generative models such as Naive Bayes.

Cyclic models and recurrent neural networks, which capture temporal aspects in modeling.

Reinforcement learning, which captures the learning of agents and is a much more general setting of learning machines.

The relationship between AI, the brain, and our society and the impact of machine learning on our society.
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