Brotkrumen-Navigation
Reiter
Python Notebook and Slides
The course begins with an overview of what machine learning is, how it differs from traditional programming, and the different types of machine learning. We will then examine some examples of modern machine-learning applications, including image recognition, speech recognition, and natural language processing.
Afterward, we dive into linear regression, which is a fundamental machine-learning technique for predicting a continuous output variable based on one or more input variables. We will cover the basic concepts of linear regression, including the cost function, gradient descent, and regularization.
We also cover the concepts of bias and variance and how they can impact the accuracy of machine-learning models.
Finally, we will introduce maximum likelihood, a powerful statistical technique used to estimate the parameters of a model by maximizing the likelihood of observing the data. We will show how maximum likelihood can be used to estimate the parameters of linear regression models and other types of machine learning models.
Afterward, we dive into linear regression, which is a fundamental machine-learning technique for predicting a continuous output variable based on one or more input variables. We will cover the basic concepts of linear regression, including the cost function, gradient descent, and regularization.
We also cover the concepts of bias and variance and how they can impact the accuracy of machine-learning models.
Finally, we will introduce maximum likelihood, a powerful statistical technique used to estimate the parameters of a model by maximizing the likelihood of observing the data. We will show how maximum likelihood can be used to estimate the parameters of linear regression models and other types of machine learning models.
Next up is a tutorial about a linear regression model written in pure python afterward a comparison to the linear regression model in the python modul sklearn.