Foundations of Machine Learning

Foundations of Machine Learning

(0 reviews)
Share icon
Coursera With GroupifyAI

Develop and evaluate machine learning models using regression, trees, and unsupervised techniques to address various business challenges.

Key AI Functions:Logistic Regression,Unsupervised Learning,Data Pre-Processing,Linear Regression,Decision Tree

Description for Foundations of Machine Learning

Features of Course

  • Utilize the varied stages of a typical machine learning workflow to develop machine learning models.
  • Evaluate the efficacy of machine learning models by employing suitable metrics for a variety of business challenges.
  • Create machine learning models that are based on regression and trees to make predictions about pertinent business issues.
  • Examine business issues that could be resolved by employing unsupervised machine learning models to extract value from data.
  • Level: Beginner

    Certification Degree: Yes

    Languages the Course is Available: 21

    Offered by: On Coursera provided by Fractal Analytics

    Duration: 25 hours (approximately)

    Schedule: Flexible

    Reviews for Foundations of Machine Learning

    0 / 5

    from 0 reviews

    Ease of Use

    Ease of Customization

    Intuitive Interface

    Value for Money

    Support Team Responsiveness

    Alternative Tools for Foundations of Machine Learning

    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

    Develop applications that are intelligent. In four practical courses, acquire a comprehensive understanding of the fundamentals of machine learning.

    #Data Clustering Algorithms #Machine Learning
    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

    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