
Learn the basic concepts and terminology of machine learning.
Learn how to read data from a CSV file and display it as a dataframe using the Polars library in Python.
Perform descriptive statistics and create distribution plots.
Clean the Ram and Weight columns.
Clean the Memory column
Additional cleaning the Memory column
Clean the Screen Resolution column.
Clean the CPU column.
Clean the GPU column.
Clean the Operating System column.
Creating the Clock Speed column.
Choose the columns to be used in model building.
Learn how to convert string data to numeric data with one-hot-encoding.
Learn how to separate the data into independent and dependent variables.
Build a dummy regressor model.
Build a linear regression model.
Build a decision tree model.
Build a catboost model.
Build a random forest model.
Understand what the R-squared score is.
Understand what the Mean Squared Error is.
Understand what the Mean Absolute Error is.
Learn how to use a residual plot to evaluate your model.
Tune the hyperparameters of a regression model.
Tune the hyperparameters of a decision tree model.
Tune the hyperparameters of a catboost model.
Use GridSearchCV to pick the values for the hyperparameters.
Create one script that performs all the data transformations we've learned, implements the model and produces the R-squared score.
Learn how to deploy the model to production using MLFLow.
Machine learning (ML) and AI are the key drivers of innovation today. Understanding how these models work can help you apply ML techniques effectively.
In this course, expert instructor Joram Mutenge shows you how to master machine learning essentials by leveraging Python and the high-performance Polars library for advanced data manipulation.
You will build an end-to-end machine learning application to predict laptop prices. Building this ML application will help you gain hands-on experience in data exploration, data processing, model creation, model evaluation, model tuning, and model deployment with MLFlow.
Learn from a Data Science Practioner
Joram has a master’s degree in Data Science from the University of Illinois Urbana-Champaign, and currently works in data at a manufacturing company building demand forecasting models. He has years of experience building and deploying machine learning models. In this course, he shares the lessons he has learned along the way.
Making the most of this course
The modules in this course build on top of each other. Learn by following the order in which these modules are presented. This will help you understand the material better. To further cement the understanding, type out the code and run it on your computer instead of passively watching. Finally, apply the knowledge learned to your own dataset.