Build a Machine Learning Model
Learn to build machine learning models with Python.
Includes Python 3, PyTorch, scikit-learn, matplotlib, pandas, Jupyter Notebook, and more.
Skill level
BeginnerTime to complete
Average based on combined completion rates — individual pacing in lessons, projects, and quizzes may vary23 hoursProjects
10Prerequisites
2 coursesWe suggest you complete the following courses before you get started with Build a Machine Learning Model:- Learn Python for Data Science
- Linear Algebra
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. Along the way, you will create real-world projects to demonstrate your new skills, from basic models all the way to neural networks.
Skills you'll gain
- Create machine learning models
- Evaluate model performance
- Build supervised and unsupervised models
- Build neural networks in PyTorch
Syllabus
10 units • 17 lessons • 10 projects • 12 quizzes- 1
Introduction to Machine Learning
Welcome to the world of machine learning! You will learn some of the fundamental concepts behind machine learning.
- 2
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.
- 3
Regression Cumulative Project
Practice your regression skills on a real-world dataset provided by Yelp!
- 4
Supervised Learning: Introduction to Classification
Learn to classify data using some of the most famous supervised machine learning models.
- 5
Supervised Learning: Advanced Classification
Learn some of the more complicated supervised machine learning models.
- 6
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.
- 7
Unsupervised Learning
Learn how unsupervised machine learning models work by implementing the K-Means clustering algorithm.
Certificate of completion available with Plus or Pro
Earn a certificate of completion and showcase your accomplishment on your resume or LinkedIn.
Projects in this skill path
- practice Project
Honey Production
Fit a line to data about the honeybee population decline in the United States. - practice Project
Cancer Classifier
Classify tumors as either malignant or benign using K-Nearest Neighbors. - practice Project
Predict Credit Card Fraud
Use Logistic Regression to predict credit card fraud.
Earn a certificate of completion
Show your network you've done the work by earning a certificate of completion for each course or path you finish.- Show proofReceive a certificate that demonstrates you've completed a course or path.
- Build a collectionThe more courses and paths you complete, the more certificates you collect.
- Share with your networkEasily add certificates of completion to your LinkedIn profile to share your accomplishments.
Reviews from learners
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Related resources
- Article
What is Scikit-Learn?
Open-source ML library for Python. Built on NumPy, SciPy, and Matplotlib. - Article
Regression vs. Classification
Learn about the two types of Supervised Learning algorithms. - Article
Supervised vs. Unsupervised Learning: what's the difference?
Learn the key differences between supervised and unsupervised learning in machine learning, with real-world examples.
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What's included in skill paths
Practice Projects
Guided projects that help you solidify the skills and concepts you're learning.Assessments
Auto-graded quizzes and immediate feedback help you reinforce your skills as you learn.Certificate of Completion
Earn a document to prove you've completed a course or path that you can share with your network.