Gain insights into machine learning algorithms like regression, deep learning, and SVMs. Learn about Python, probability theory, and optimization through practical coding exercises. Delve into ML intricacies.

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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

**(True or False)** The delta-function is useful for writing discrete events in a continuous form.

(True or False) The delta-function is useful for writing discrete events in a continuous form.

**(True or False)** The median is never equal to the mean in cases of symmetric distribution. 

(True or False) The median is never equal to the mean in cases of symmetric distribution.

The sample mean is a biased estimate, while the sample variance of the naive estimate $E(X^2)$ is unbiased.

The sample mean is an unbiased estimate, while the sample variance of the naive estimate $E(X^2)$ is biased.

The sample mean is a biased estimate, and the sample variance of the naive estimate $E(X^2)$ is also biased.

The sample mean is an unbiased estimate, and the sample variance of the naive estimate $E(X^2)$ is also unbiased.

Which of the following statements about the mean and variance is true?

Which of the following statements about the mean and variance is true?

**(Fill in the blank)** **The central limit theorem** states that the average (normalized sum) of $n$ random variables that are drawn from any distribution with mean $\mu$ and variance $\sigma$ is approximately normally distributed with mean $\mu$ and variance ________ for a sufficiently large sample size $n$.

(Fill in the blank) The central limit theorem states that the average (normalized sum) of <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>n</mi></mrow><annotation encoding="application/x-tex">n</annotation></semantics></math>n random variables that are drawn from any distribution with mean <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>μ</mi></mrow><annotation encoding="application/x-tex">\mu</annotation></semantics></math>μ and variance <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>σ</mi></mrow><annotation encoding="application/x-tex">\sigma</annotation></semantics></math>σ is approximately normally distributed with mean <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>μ</mi></mrow><annotation encoding="application/x-tex">\mu</annotation></semantics></math>μ and variance ________ for a sufficiently large sample size <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>n</mi></mrow><annotation encoding="application/x-tex">n</annotation></semantics></math>n.

**(Fill in the blank)** The ________ of logarithms is equal to the logarithm of the quotient.

(Fill in the blank) The ________ of logarithms is equal to the logarithm of the quotient.

$$
p(x \mid y)=\frac{p(y \mid x) p(x)}{p(y)}
$$

$$
p(x \mid y)=\frac{p(x \mid y) p(x)}{p(y)}
$$

$$
p(x \mid y)=\frac{p(x \mid y) p(y)}{p(y)}
$$

$$
p(x \mid y)=\frac{p(x \mid y) p(x)}{p(x)}
$$

Which of the following is the formula for Bayes' rule?

Which of the following is the formula for Bayes’ rule?

**(True or False)** Generally, total independence does not imply conditional independence.

(True or False) Generally, total independence does not imply conditional independence.

$$ p(x)=\left\{\begin{array}{ll}
\frac{1}{b-a} & \text { for } a \leq x \leq b \\
0 & \text { otherwise. }
\end{array}\right
$$

$$
p(x)=\frac{1}{\sigma \sqrt{2 \pi}} e^{\frac{-(x-\mu)^{2}}{2 \sigma^{2}}}
$$

$$
p(x)=\frac{x^{(\nu-2) / 2} e^{-x / 2}}{2^{\nu / 2} \Gamma(\nu / 2)}
$$

$$
P(x)=\left(\begin{array}{l}
n \\
x
\end{array}\right) p^{x}(1-p)^{n-x}
$$

Which of the following is the probability density function of uniform distribution?

Which of the following is the probability density function of uniform distribution?

$$ p(x)=\left\{\begin{array}{ll}
\frac{1}{b-a} & \text { for } a \leq x \leq b \\
0 & \text { otherwise }
\end{array}\right.
$$

Which of the following is the probability density function of the binomial distribution?

Which of the following is the probability density function of the binomial distribution?

Test yourself on basic probability theory.

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