4.8 / 5
We will cover the following tasks in 45 minutes:
Introduction and Overview
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.
Loading the Data and Importing Libraries
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.
Removing the Index Column
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.
Exploratory Data Analysis
It’s good practice to first visualize the data before doing any analysis and model building. If the data is high dimensional, at least examine few slices using simple techniques like box plots. In this task, we will look at the distributions of our response variable ,
Sales, and also looked at the distributions of our predictors,
Relationship between Predictors and Response
Here we visualize pairwise correlations between our predictors and response. We also create a heatmap of the corresponding correlation matrix. This is done so as to ascertain whether there exists any relationship between the sales revenue and the expenditure on the various advertisement channels.
Creating the Simple Linear Regression Model
Linear regression is an approach for modeling the relationship between a scalar dependent response variable y and one or more predictors (or independent variables) denoted X. The case where we have just one predictor is known as simple linear regression.
In this task, we are going to use the
LinearRegression estimator from
sklearn.model_selection to create our ordinary least squares (OLS) linear regression model. We will use
train_test_split to split our data into training and test sets.
Evaluation and Model Parameters
In this task we will use the
summary() methods to produce human-readable output of our model coefficients and the OLS regression results.
Making Predictions with the Model
Now that we have trained our OLS regression model on the training data, we will use
predict() method to make predictions on the test data. Note that the predictions are made on data the model has never seen during train time.
Model Evaluation Metrics
Evaluation metrics for classification problems, such as accuracy, are not useful regression problems. We need a metric designed to evaluate continuous values. In this task, we calculate and implement the three most common evaluation metrics for regression: Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE).
It's a very good idea to have both videos and the coding environment available at the same time, however with only one display to spare both the screens become pretty small, which is getting hard on the eye after some time.
sometimes i had difficulties with keyboard when used caps lock i can't switch back
Potential Logistical Issues for Students On a large screen (I did it on a 17" Laptop) or with 2 screens it is fine to read both the notebook and the video. In this scenario using this platform would be more engaging than just watching a video (in my opinion). However, for those on mobile devices or using relatively small screens it may be a lot more practical to watch the videos and then do the notebook separately. It might also be prudent to ensure there is a sufficient time buffer beyond the predicted time to complete to minimize the potential of the cloud session timing out prior to the student finishing.
I would like to download the Jupitar file but, I could not.
I hope the project will finally show up in Coursera as completed this time.
Had a great learning experience with this platform. Hope there will be a lot of sessions that will be run in rhyme. kudos!
There are some typos, but overall it was a good experience. Is there a way for user to download the jupyter notebook to their own desktop?
I tried to think ahead. I was hoping the voice would motivate to do that more.
Session too short, problem with  for typing the code and screen too small (mac bookAir).
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.