Machine Learning: Concepts and Applications
Master machine learning theory and practice. Learn Python, Pandas, Scikit-learn, and TensorFlow for ML modeling.
Master machine learning theory and practice. Learn Python, Pandas, Scikit-learn, and TensorFlow for ML modeling.
Dive into the world of machine learning with this comprehensive course from the University of Chicago. Covering both theory and practice, you'll learn to use Python and industry-standard libraries like Pandas, Scikit-learn, and TensorFlow to ingest, explore, and prepare data for modeling. Master a wide range of techniques including linear regression, logistic regression, SVMs, decision trees, ensembles, clustering, PCA, HMMs, and deep learning. Gain hands-on experience with real-world datasets, focusing on public policy applications. Ideal for those with basic Python and linear algebra knowledge, this course provides a strong foundation for deeper, specialized study in machine learning.
3.6
(17 ratings)
3,233 already enrolled
Instructors:
English
Powered by
What you'll learn
Understand the machine learning pipeline from data ingestion to model deployment
Master linear regression techniques including OLS, regularization, and polynomial expansion
Apply classification methods such as logistic regression, SVMs, and Naive Bayes
Implement and evaluate decision trees and ensemble methods like random forests
Explore unsupervised learning techniques including various clustering methods
Utilize dimensionality reduction techniques, focusing on Principal Component Analysis (PCA)
Skills you'll gain
This course includes:
3.56 Hours PreRecorded video
20 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
Created by
Provided by
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 9 modules in this course
This comprehensive course covers the full spectrum of machine learning concepts and applications. Starting with the fundamentals of data ingestion and exploration using Pandas, it progresses through various modeling techniques. Topics include linear and logistic regression, regularization, SVMs, decision trees, ensemble methods, clustering, PCA, hidden Markov models, and deep learning. The course emphasizes both theoretical understanding and practical implementation using Python and libraries like Scikit-learn and TensorFlow. Real-world datasets, primarily from public policy, are used throughout, providing a strong foundation for applied machine learning.
Machine Learning and the Machine Learning Pipeline
Module 1 · 4 Hours to complete
Least Squares and Maximum Likelihood Estimation
Module 2 · 5 Hours to complete
Basis Functions and Regularization
Module 3 · 3 Hours to complete
Model Selection and Logistic Regression
Module 4 · 3 Hours to complete
More Classifiers: SVMs and Naive Bayes
Module 5 · 4 Hours to complete
Tree-Based Models, Ensemble Methods, and Evaluation
Module 6 · 5 Hours to complete
Clustering Methods
Module 7 · 3 Hours to complete
Dimensionality Reduction and Temporal Models
Module 8 · 3 Hours to complete
Deep Learning
Module 9 · 3 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructor
Leading Researcher in Computer Networking and Cybersecurity
Nick Feamster is a professor in the Department of Computer Science, having earned his PhD from MIT in 2005, along with his SB and MEng degrees in Electrical Engineering and Computer Science in 2000 and 2001, respectively. His research encompasses various aspects of computer networking and networked systems, focusing on the design, measurement, and analysis of network routing protocols, operations, security, and anonymous communication systems. In recognition of his contributions to cybersecurity, particularly in spam filtering, Feamster was awarded the Presidential Early Career Award for Scientists and Engineers (PECASE) in December 2008. He has also received several prestigious honors, including a Sloan Research Fellowship and the NSF CAREER award. His influential papers have garnered awards at major conferences such as SIGCOMM and Usenix Security.
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
3.6 course rating
17 ratings
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.