The linear and logistic regression course offers a detailed introduction to fundamental statistical and machine learning algorithms, particularly focusing on regression techniques. The course begins with simple linear regression and progresses to multiple linear regression, equipping students with the ability to analyze relationships between multiple variables. Finally, it covers logistic regression, a powerful tool for classification problems.
In this lesson, we kick off the course with an introduction to machine learning and why linear regression is such an important algorithm.
In this lesson, you'll use Python to fit linear regression models, as well as understand how to interpret the results of linear models.
In this lesson, you'll learn to apply multiple linear regression models in Python. Then you'll learn how to interpret the results and understand if your model fits well.
In this lesson, you'll learn to apply logistic regression models in Python as well as how to interpret logistic regression results.
In this project, you will build a linear model and interpret the results of the model. Specifically, you will look at what variables are related to salaries.
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Basic probability. Pandas. Inferential statistics. Basic Python. NumPy. Basic descriptive statistics
MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: http://ocw.mit.edu/18-650F16 Instructor: Philippe Rigollet This course offers an in-depth the theoretical foundations for statistical methods that are useful in many applications. The goal is to understand the role of mathe…
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Led by Andrew Ng, this course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (cluster…
View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston In these lectures, Prof. Patrick Winston introduces the 6.034 material from a conceptual, big-picture perspective. Topics include reasoning, search, constraints, learning, representations, architectures, and probabili…
