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

**(Fill in the blank)** The number of correctly predicted negative samples is called ______.

(Fill in the blank) The number of correctly predicted negative samples is called ______.

$$
\frac{\mathrm{TP}+\mathrm{TN}}{\mathrm{TP}+\mathrm{TN}+\mathrm{FP}+\mathrm{FN}}
$$

$$
\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}}
$$

$$
\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}}
$$

Which of the following is the correct formula for Accuracy?

Which of the following is the correct formula for Accuracy?

$$
\frac{\text { FP }}{\text { FP+TN }}
$$

Which of the following is the correct formula of Precision?

Which of the following is the correct formula of Precision?

**(True or False)** It is of utmost importance to ensure that predictions are made on data that has never been seen in the training set before.

(True or False) It is of utmost importance to ensure that predictions are made on data that has never been seen in the training set before.

**(True or False)** An equal amount of data must be used from both positive and negative classes in the training set to counter the imbalance of data.

(True or False) An equal amount of data must be used from both positive and negative classes in the training set to counter the imbalance of data.

**(True or False)** The data points far from a regression line are expected to fluctuate as per the main idea in support vector regression. 

(True or False) The data points far from a regression line are expected to fluctuate as per the main idea in support vector regression.

Test yourself on machine learning with sklearn.

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