Supervised ML: Regression

Supervised ML: Regression

(0 reviews)
Share icon

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.

Key AI Functions:Linear Regression,Machine Learning (ML) Algorithms,Ridge Regression,Supervised Learning,Regression Analysis

Description for Supervised ML: Regression

Features of Course

  • Learn about regression in supervised machine learning, covering training models for continuous outcomes, error measures, and Elastic Net, LASSO, and Ridge regularization.
  • Learn to discern classification and regression applications, develop and apply linear regression models, and choose error metrics.
  • Regularization prevents overfitting; use it to improve regression models.
  • Aspiring data scientists who want to use supervised machine learning regression in business must know data cleaning, exploratory data analysis, mathematics, linear algebra, probability, statistics, and Python programming.
  • Level: Intermediate

    Certification Degree: Yes

    Languages the Course is Available: 22

    Offered by: On Coursera provided by IBM

    Duration: 20 hours (approximately)

    Schedule: Flexible

    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.

    #Logistic Regression #Unsupervised Learning
    icon

    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.

    #Artificial Intelligence #Python (Programming Language)
    icon

    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.

    #Logistic Regression #Artificial Neural Network
    icon

    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.

    #Statistical Hypothesis Testing #Machine Learning (ML) Algorithms
    icon

    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.

    #Bayesian Statistics #Mathematics
    icon

    Learn to build and train supervised machine learning models for binary classification and prediction tasks using Python with NumPy and scikit-learn libraries.

    #Linear Regression #Regularization to Avoid Overfitting
    icon

    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.

    #Unsupervised Learning #Machine Learning
    icon

    Real-World Applications of Machine Learning. Develop proficiency in the implementation of a machine learning undertaking.

    #Project Management #Machine Learning (ML) Algorithms
    icon

    Learn fundamental machine learning principles, including K nearest neighbor, linear regression, and model analysis, with prerequisites of Python programming and basic mathematics.

    #Machine Learning #Python
    icon

    Learn to develop, assess, and enhance machine learning models using Python libraries, covering introductory deep learning, supervised, and unsupervised learning algorithms.

    #Unsupervised Learning #Python Programming
    icon