Build a Machine Learning Model with Python
Learn to build machine learning models with Python.
Includes Python 3, scikit-learn, matplotlib, pandas, Jupyter Notebooks, and more.
Time to complete7 weeks
Certificate of completionYes
Prerequisites2 coursesWe suggest you complete the following courses before you get started with Build a Machine Learning Model with Python:
- Getting Started with Python for Data Science
- Python for Data Science: Working with Data
About this skill path
More data is created and collected every day. Machine learning models can find patterns in big data to help us make data-driven decisions. In this skill path, you will learn to build machine learning models using regression, classification, and clustering methods. Along the way, you will create real-world projects to demonstrate your new skills.
Skills you'll gain
- Create neural nets from scratch
- Evaluate model performance
- Build supervised and unsupervised models
Syllabus10 units • 16 lessons • 9 projects • 11 quizzes
Introduction to Machine Learning
Welcome to the world of machine learning! You will learn some of the fundamental concepts behind machine learning.
Supervised Learning: Regression
Use linear regression or multiple linear regression to fit a line to data. Using this line, you can make predictions about future data.
Regression Cumulative Project
Practice your regression skills on a real-world dataset provided by Yelp!
Supervised Learning: Introduction to Classification
Learn to classify data using some of the most famous supervised machine learning models.
Supervised Learning: Advanced Classification
Learn some of the more complicated supervised machine learning models.
Supervised Machine Learning Cumulative Project
In this cumulative project, use your understanding of a variety of supervised machine learning models to analyze social media data.
Learn how unsupervised machine learning models work by implementing the K-Means clustering algorithm.
Hands-on learningDon't just watch or read about someone else coding — write your own code live in our online, interactive platform. You'll even get AI-driven recommendations on what you need to review to help keep you on track.
Projects in this skill path
Honey ProductionFit a line to data about the honeybee population decline in the United States.
Cancer ClassifierClassify tumors as either malignant or benign using K-Nearest Neighbors.
Predict Titanic SurvivalIn this project you will use a Logistic Regression model to predict whether or not a passenger survived the sinking of the RMS Titanic.
Reviews from learners
- The progress I have made since starting to use codecademy is immense! I can study for short periods or long periods at my own convenience - mostly late in the evenings.ChrisCodecademy Learner @ USA
- I felt like I learned months in a week. I love how Codecademy uses learning by practice and gives great challenges to help the learner to understand a new concept and subject.RodrigoCodecademy Learner @ UK
- Brilliant learning experience. Very interactive. Literally a game changer if you're learning on your own.John-AndrewCodecademy Learner @ USA
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Looking for something else?
What is Scikit-Learn?Open-source ML library for Python. Built on NumPy, SciPy, and Matplotlib.
Regression vs. ClassificationLearn about the two types of Supervised Learning algorithms.
Introduction to Regression AnalysisThis article is a brief introduction to the formal theory (otherwise known as Math) behind regression analysis.
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Linear Regression in PythonLearn how to fit, interpret, and compare linear regression models in Python.Intermediate6 hours
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What's included in skill paths
Practice ProjectsGuided projects that help you solidify the skills and concepts you're learning.
AssessmentsAuto-graded quizzes and immediate feedback help you reinforce your skills as you learn.
Certificate of CompletionEarn a document to prove you've completed a course or path that you can share with your network.