Machine Learning Essentials
Master fundamental machine learning concepts including statistical learning, linear regression, and classification using Python programming.
Master fundamental machine learning concepts including statistical learning, linear regression, and classification using Python programming.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full AI and Machine Learning Essentials with Python Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
English
Powered by
What you'll learn
Review probability basics and statistical learning framework
Implement linear regression models using Python
Analyze coefficient uncertainty and confidence intervals
Work with categorical and nonlinear inputs
Develop classification solutions using logistic regression
Apply maximum likelihood estimation techniques
Skills you'll gain
This course includes:
3.25 Hours PreRecorded video
12 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 4 modules in this course
This comprehensive course covers essential machine learning concepts and techniques. Starting with statistical learning fundamentals, students progress through linear regression and classification problems. The curriculum emphasizes both theoretical understanding and practical implementation using Python. Topics include regression functions, parametric models, bias-variance tradeoff, linear and logistic regression, and classification techniques. Through hands-on programming assignments, students develop skills in implementing machine learning solutions and analyzing real-world data.
Statistical Learning
Module 1 · 4 Hours to complete
Linear Regression
Module 2 · 3 Hours to complete
Extended Linear Regression
Module 3 · 5 Hours to complete
Classification
Module 4 · 4 Hours to complete
Fee Structure
Instructors
Leading Scholar in Natural Language Processing and Crowdsourcing
Chris Callison-Burch is an associate professor of Computer and Information Science at the University of Pennsylvania, where his work has positioned him as a thought leader in natural language processing (NLP) and crowdsourcing. Before joining Penn, he was a research faculty member at the Center for Language and Speech Processing at Johns Hopkins University, contributing significantly to advancements in the field during his six-year tenure.Chris has held prominent roles in major conferences and organizations, including serving as the General Chair of the ACL 2017 conference, Program Co-Chair for EMNLP 2015, Chair of the Executive Board of NAACL (2011–2013), and Secretary-Treasurer for SIGDAT (2015–2017). His editorial contributions span leading journals such as Transactions of the ACL (TACL) and Computational Linguistics.With over 100 publications that have been cited more than 10,000 times, Chris's research has had a profound impact on computational linguistics. Recognized as a Sloan Research Fellow, he has received prestigious faculty research awards from Google, Microsoft, Amazon, and Facebook. His work has also been supported by DARPA and the National Science Foundation (NSF).Chris's research interests focus on the intersections of natural language processing, machine translation, and the innovative use of crowdsourcing to tackle complex computational challenges. His contributions continue to shape the future of AI-driven language technologies and their practical applications.
Associate Professor and Graduate Chair
Honors and Awards: 2017 National Science Foundation CAREER Award, 2018 Best Paper Award by the IEEE Control Systems Magazine, 2019 Best Paper Award by the IEEE Transactions on Network Science and Engineering runner up Research Expertise: Networks, Dynamics, and Data Sciences Victor M. Preciado is an Associate Professor and Graduate Chair in the Department of Electrical and Systems Engineering at the University of Pennsylvania, where he is affiliated with the Networked and Social Systems Engineering program, the Warren Center for Network and Data Sciences, and the Applied Math and Computational Science program. Victor received the Ph.D. degree in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology under the supervision of Prof. George Verghese and was a postdoctoral researcher at the GRASP lab working with Prof. Ali Jadbabaie. His main research interests lie at the intersection of Networks, Dynamics, and Data Sciences; in particular, in using innovative mathematical and computational approaches to model and control complex, high-dimensional dynamical systems. Relevant applications of this line of research can be found in the context of socio-technical networks, healthcare operations, critical technological infrastructure, and brain dynamics. Member of: Warren Center for Network and Data Sciences Affiliations: Applied Mathematics and Computational Science Education: PhD Electrical Engineering and Computer Science 2008 - Massachusetts Institute of Technology
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.
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.