This course gives you a comprehensive introduction to both the theory and practice of machine learning. You will learn to use Python along with industry-standard libraries and tools, including Pandas, Scikit-learn, and Tensorflow, to ingest, explore, and prepare data for modeling and then train and evaluate models using a wide variety of techniques. Those techniques include linear regression with ordinary least squares, logistic regression, support vector machines, decision trees and ensembles, clustering, principal component analysis, hidden Markov models, and deep learning.
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Machine Learning: Concepts and Applications
The University of ChicagoAbout this Course
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Shareable Certificate
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Coursera Labs
Includes hands on learning projects.
Learn more about Coursera Labs Intermediate Level
Basic Python and Linear Algebra
Approx. 37 hours to complete
English
Skills you will gain
- Artificial Neural Network
- Machine Learning
- Unsupervised Learning
- Statistical Classification
- regression
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Coursera Labs
Includes hands on learning projects.
Learn more about Coursera Labs Intermediate Level
Basic Python and Linear Algebra
Approx. 37 hours to complete
English
Offered by
Syllabus - What you will learn from this course
5 hours to complete
Machine Learning and the Machine Learning Pipeline
5 hours to complete
6 videos (Total 50 min)
5 hours to complete
Least Squares and Maximum Likelihood Estimation
5 hours to complete
4 videos (Total 17 min)
3 hours to complete
Basis Functions and Regularization
3 hours to complete
4 videos (Total 25 min)
3 hours to complete
Model Selection and Logistic Regression
3 hours to complete
4 videos (Total 24 min)
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