RiseUpp Logo
Educator Logo

Introduction to Machine Learning: Supervised Learning

Master Python-based machine learning foundations, from regression to support vector machines. Ideal for intermediate programmers.

Master Python-based machine learning foundations, from regression to support vector machines. Ideal for intermediate programmers.

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 Machine Learning: Theory and Hands-on Practice 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.

3.3

(55 ratings)

14,054 already enrolled

Instructors:

English

Powered by

Provider Logo
Introduction to Machine Learning: Supervised Learning

This course includes

39 Hours

Of Self-paced video lessons

Intermediate Level

Completion Certificate

awarded on course completion

Free course

What you'll learn

  • Use modern machine learning tools and Python libraries effectively

  • Implement and evaluate logistic regression models

  • Handle linearly-inseparable data with advanced techniques

  • Design and optimize decision tree algorithms

  • Apply ensemble methods for improved model performance

  • Master support vector machines and kernel methods

Skills you'll gain

Machine Learning
Python Programming
Supervised Learning
Linear Regression
Logistic Regression
Decision Trees
Support Vector Machines
Ensemble Methods
scikit-learn
Model Optimization

This course includes:

5.7 Hours PreRecorded video

9 quizzes

Access on Mobile, Tablet, Desktop

FullTime access

Shareable certificate

Closed caption

Get a Completion Certificate

Share your certificate with prospective employers and your professional network on LinkedIn.

Certificate

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.

icon-0icon-1icon-2icon-3icon-4

There are 6 modules in this course

This comprehensive course covers fundamental supervised machine learning concepts and their practical implementation using Python. Students learn various algorithms including linear regression, logistic regression, decision trees, and support vector machines. The curriculum emphasizes both theoretical understanding and hands-on practice, with extensive programming assignments using popular libraries like scikit-learn. Topics include model evaluation, hyperparameter tuning, and ensemble methods, providing a solid foundation for real-world machine learning applications.

Introduction to Machine Learning, Linear Regression

Module 1 · 7 Hours to complete

Multilinear Regression

Module 2 · 6 Hours to complete

Logistic Regression

Module 3 · 6 Hours to complete

Non-parametric Models

Module 4 · 6 Hours to complete

Ensemble Methods

Module 5 · 6 Hours to complete

Kernel Method

Module 6 · 7 Hours to complete

Fee Structure

Instructor

Geena Kim
Geena Kim

3.1 rating

29 Reviews

23,309 Students

3 Courses

Adjunct Professor

Dr. Geena Kim is an Adjunct Professor in the Computer Science Department at the University of Colorado Boulder, where she specializes in deep learning and machine learning. She holds a Ph.D. from UC Berkeley and has extensive experience in both academia and industry, currently serving as a Research Scientist at Amazon. Her career also includes entrepreneurial ventures and technical advisory roles for Internet of Things (IoT) startups in the Bay Area, showcasing her versatile expertise in cutting-edge technology.Dr. Kim teaches several courses that focus on machine learning techniques, including "Introduction to Deep Learning," "Introduction to Machine Learning: Supervised Learning," and "Unsupervised Algorithms in Machine Learning." Her research interests encompass deep learning, computer vision, and medical image analysis, contributing to advancements in these fields through innovative applications. With a strong commitment to education and research, Geena Kim continues to influence the next generation of computer scientists and data analysts at CU Boulder.

Introduction to Machine Learning: Supervised Learning

This course includes

39 Hours

Of Self-paced video lessons

Intermediate Level

Completion Certificate

awarded on course completion

Free course

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.

3.3 course rating

55 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.