Wrap Up

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.

Congratulations!