Basic Statistics and Machine Learning
Linear regression is a basic and commonly used type of predictive analysis. Linear regression is one of the easiest and most popular Machine Learning algorithms. It is a statistical method that is used for predictive analysis. Linear regression makes predictions for real or numeric variables such as sales, salary, age, product price, etc. A linear regression model describes the relationship between a dependent variable, y, and one or more independent variables, X. The dependent variable is also called the response variable. Independent variables are also called explanatory or predictor variables. Continuous predictor variables are also called covariates, and categorical predictor variables are also called factors. The matrix X of observations on predictor variables is usually called the design matrix.
By the end of the course, you will have a solid understanding of how to use Python to build linear regression models and make accurate predictions. You will be able to apply your new skills to a wide range of machine learning and data science projects. This course teaches you about one popular technique used in machine learning, data science and statistics: linear regression. We cover the theory from the ground up. derivation of the solution, and applications to real-world problems. We show you how one might code their own linear regression module in Python.
Linear regression is the simplest machine learning model you can learn, yet there is so much depth that you’ll be returning to it for years to come. That’s why it’s a great course if you’re interested in taking your first steps in the fields of deep learning, machine learning, data science or statistics
Who this course is for:
- Anyone who wants to learn about data and analytics
- Data Engineers, Analysts, Architects, Software Engineers, IT operations, Technical managers
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