fundamentals_of_machine_learning.tar.gz

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The machine learning field is rapidly advancing today due to the availability of large datasets and the ability to process big data efficiently. Moreover, several new techniques have produced groundbreaking results for standard machine learning problems.

This course provides a detailed description of different machine learning algorithms and techniques, including regression, deep learning, reinforcement learning, Bayes nets, support vector machines (SVMs), and decision trees. The course also offers sufficient mathematical details for a deeper understanding of how different techniques work. An overview of the Python programming language and the fundamental theoretical aspects of ML, including probability theory and optimization, is also included. The course contains several practical coding exercises as well.

By the end of the course, you will have a deep understanding of different machine-learning methods and the ability to choose the right method for different applications.

Mastering Machine Learning Theory and Practice

Generate feature vectors for instances of the class they represent.

Characterize the class with all its variations.

Characterize the class with some of its variations.

**(Select all that apply)** Which of the following should a generative model be able to do?

(Select all that apply) Which of the following should a generative model be able to do?

**(True or False)** Generative models can't be used to generate examples of class objects.

(True or False) Generative models can’t be used to generate examples of class objects.

**(True or False)** The big advantage of naive Bayes compared to more complex causal models is that the number of parameters is fairly small.

(True or False) The big advantage of naive Bayes compared to more complex causal models is that the number of parameters is fairly small.

**(True or False)** Logistic regression assumes that the class distributions are Gaussian.

(True or False) Logistic regression assumes that the class distributions are Gaussian.

**(True or False)** In a generative adversarial network $\text{(GAN)}$, the discriminator network is trained on a combination of real and fake examples so that it can discriminate between network-generated examples and real examples.

(True or False) In a generative adversarial network <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>(GAN)</mtext></mrow><annotation encoding="application/x-tex">\text{(GAN)}</annotation></semantics></math>(GAN), the discriminator network is trained on a combination of real and fake examples so that it can discriminate between network-generated examples and real examples.

**(True or False)** Self-supervised or unsupervised learning doesn't mean that the learning is not guided at all. Rather, it means that the learning follows specific principles that are used to guide the organization of the data itself.

(True or False) Self-supervised or unsupervised learning doesn’t mean that the learning is not guided at all. Rather, it means that the learning follows specific principles that are used to guide the organization of the data itself.

**(Fill in the blank)** In a generative adversarial network $\text{(GANs)}$, the discriminator network is fed with ________.

(Fill in the blank) In a generative adversarial network <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>(GANs)</mtext></mrow><annotation encoding="application/x-tex">\text{(GANs)}</annotation></semantics></math>(GANs), the discriminator network is fed with ________.

In a generative adversarial network, $\text{(GAN)}$, fake inputs are generated by the ________ network.

In a generative adversarial network, <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>(GAN)</mtext></mrow><annotation encoding="application/x-tex">\text{(GAN)}</annotation></semantics></math>(GAN), fake inputs are generated by the ________ network.

Getting started

Introduction

Scientific Programming with Python

Machine Learning with Sklearn

Neural Networks and Keras

Regression and Optimization

Basic Probability Theory

Probabilistic Regression and Bayes Nets

Generative Models

Cyclic Models and Recurrent Neural Networks

Reinforcement Learning

Artificial intelligence, the Brain, and Our Society

Conclusion

Quiz