Supervised ML: Regression
Besides Python programming and data science fundamentals, the course covers supervised machine learning regression, which includes training models for continuous outcomes, error metrics, Elastic Net, LASSO, Ridge regularization, and data science fundamentals for aspiring data scientists.
Description for Supervised ML: Regression
Level: Intermediate
Certification Degree: Yes
Languages the Course is Available: 22
Offered by: On Coursera provided by IBM
Duration: 20 hours (approximately)
Schedule: Flexible
Pricing for Supervised ML: Regression
Use Cases for Supervised ML: Regression
FAQs for Supervised ML: Regression
Reviews for Supervised ML: Regression
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Supervised ML: Regression
Develop and evaluate machine learning models using regression, trees, and unsupervised techniques to address various business challenges.
Investigate the field of artificial intelligence and machine learning. While investigating the transformative disciplines of artificial intelligence, machine learning, and deep learning, enhance your Python abilities.
Specialization in Machine Learning at BreakIntoAI. Master the fundamental AI concepts and cultivate practical machine learning skills in the beginner-friendly, three-course program by AI visionary Andrew Ng.
Set up for a profession in machine learning. To become job-ready in less than three months, acquire the skills and practical experience that are in high demand.
Master the AI and machine learning toolkit. Mathematics for Machine Learning and Data Science is a Specialization that is accessible to beginners. In this program, you will acquire the basic mathematics tools of machine learning, including calculus, linear algebra, statistics, and probability.
Learn to build and train supervised machine learning models for binary classification and prediction tasks using Python with NumPy and scikit-learn libraries.
Acquire knowledge of machine learning by examining actual applications. Develop the necessary skills for a vocation in one of the most pertinent areas of contemporary AI by participating in hands-on projects and completing coursework from IBM's experts.
Real-World Applications of Machine Learning. Develop proficiency in the implementation of a machine learning undertaking.
Learn fundamental machine learning principles, including K nearest neighbor, linear regression, and model analysis, with prerequisites of Python programming and basic mathematics.
Learn to develop, assess, and enhance machine learning models using Python libraries, covering introductory deep learning, supervised, and unsupervised learning algorithms.
Featured Tools
The course gives an extensive understanding of AI, which encompasses its ethical implications, neural networks, data significance, and applications.
The primary objective of this program is to integrate AI tools into education while addressing ethical standards in the development and implementation of AI.
The completion of this course provides participants with practical AI skills and certification as a CoRover Certified Professional.
Learn to train and develop image classification and object detection systems using machine learning, and deploy these models to microcontrollers.
Use Tome AI to create detailed presentation outlines, integrate relevant documents, and generate visually appealing slides through effective prompts.