Data Science

Supervised ML: Regression

(0 reviews)
Share icon
Coursera With GroupifyAI

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

  • 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

    It provides professionals with the necessary skills to define machine learning problems, prepare data, and identify applications across a variety of domains.

    #project management #machine learning (ml) algorithms
    Visit icon

    Brief Summary This course analyzes the deployment of machine learning models on Arm microcontrollers, with an emphasis on real-world applications in edge computing.

    #computer vision #tensorflow
    Visit icon

    This course equips students with the necessary business leadership skills and technical knowledge to propel the success of ML.

    #predictive analytics #ethics of artificial intelligence
    Visit icon

    Commence Your Career in Data Science. A ten-course introduction to data science, devised and instructed by distinguished professors.

    #Github #Machine Learning
    Visit icon

    The topics of this AI course include the optimization of policies in reinforcement learning, the utilization of dimensionality reduction in unsupervised learning, and the classification and definition of constraints in supervised learning.

    #Machine Learning #Artificial Intelligence
    Visit icon

    Master regression by predicting house prices, investigate regularized linear regression, manage extensive feature sets, and employ optimization algorithms to make precise predictions with large datasets.

    #Linear Regression #Ridge Regression
    Visit icon

    This course concentrates on the fundamentals of machine learning, including decision trees, k-nearest neighbors, and support vector machines. It addresses data preparation and production challenges and requires a rudimentary understanding of Python, linear algebra, and statistics.

    #Machine Learning #Supervised Learning
    Visit icon

    Comprehend the function of AI in resolving intricate problems. Develop the ability to combine human and machine intellect to make a positive real-world impact through the use of AI.

    #Topic Model #Biodiversity Monitoring
    Visit icon

    Discover the process of identifying machine learning model types, training and deploying predictive models using Azure Machine Learning's automated capabilities, developing regression, classification, and clustering models with Azure Machine Learning Designer, and deploying models seamlessly without scripting.

    #Microsoft Azure #Machine Learning
    Visit icon