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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