NA / 5
We will cover the following tasks in 1 hour and 11 minutes:
Introduction and Libraries
We will understand the Rhyme interface and our learning environment. You will be provided with a cloud desktop with Jupyter Notebooks and all the software you will need to complete the project. Jupyter Notebooks are very popular with Data Science and Machine Learning Engineers as one can write code in cells and use other cells for documentation.
We will also introduce the model we will be building as well the dataset for this Project. Finally, we will import libraries and helper functions that will be essential later in the project.
Loading the Data
In this task, we will load the very popular advertising dataset about various costs incurred on advertising by different mediums such as through TV, radio, newspaper, and the sales for a particular product. Next, we will briefly explore the data to get some basic information about what we are going to be working with.
Cleaning and preprocessing data is a vital process in data analysis and machine learning. In this task, we will take a look at our imported data and ascertain what to remove and what to keep for further analysis.
Multiple Linear Regression - Estimating Coefficients
Multiple linear regression is an approach for modeling the relationship between a scalar dependent response variable y and two or more predictors (or independent variables) denoted X.
In this task, we are going to use the
statsmodels.api to create our ordinary least squares (OLS) linear regression model. We will use
sklearn to split our data into training and test sets.
In this task, we will understand the significance of the model coefficients as they relate to the advertising data. We have already learned to interpret them geometrically, and now we will look at how the coefficients of the predictors affect the response variable `sales.
Although one can include all available predictors as features or regressors, that is most often not the smart choice. In this task, we will implement various models and determine which features to include for our inference.
Model Evaluation Metrics
Here we will learn the theory and implementation details behind Mean Absolute Error, Mean Squared Error, and Root Mean Square Error. We will then make the appropriate choice of metrics for our model.
Model Evaluation Using Train/Test Split and RMSE
Armed with our arsenal of model metrics, we will evaluate the multiple regression model on the test set to determine out of sample performance.
About the Host (Snehan Kekre)
Snehan Kekre is a Machine Learning and Data Science Instructor at Coursera. He studied Computer Science and Artificial Intelligence at Minerva Schools at KGI, based in San Francisco. His interests include AI safety, EdTech, and instructional design. He recognizes that building a deep, technical understanding of machine learning and AI among students and engineers is necessary in order to grow the AI safety community. This passion drives him to design hands-on, project-based machine learning courses on Rhyme.