AI & Machine Generators

ML & Human Learning

(0 reviews)
Share icon
Coursera With GroupifyAI

Explore the intersection of human and machine learning, covering supervised and unsupervised techniques, AI's impact on education, and applications in learning management systems, designed for educators and AI enthusiasts.

Key AI Functions:

Machine Learning,Human Learning,Supervised ML,Unsupervised ML,Artificial Intellegence

Description for ML & Human Learning

  • Investigate the distinctions between human and machine learning.
  • Explore supervised and unsupervised machine learning techniques.
  • Examine AI's practical applications in learning management systems and educational tools.
  • Critically analyze the impact and ethical implications of AI in education.
  • Level: Beginner

    Certification Degree: Yes

    Languages the Course is Available: 1

    Offered by: On Coursera provided by Illinois

    Duration: 36 hours to complete

    Schedule: Flexible

    Reviews for ML & Human Learning

    0 / 5

    from 0 reviews

    Ease of Use

    Ease of Customization

    Intuitive Interface

    Value for Money

    Support Team Responsiveness

    Alternative Tools for ML & Human Learning

    Learn to describe and implement various machine learning algorithms in Python, including classification and regression techniques, and evaluate their performance using appropriate metrics.

    #Machine Learning #regression
    Visit 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
    Visit icon

    Gain foundational knowledge of Linear Algebra and Machine Learning models, explore the scalability of SparkML and Scikit-Learn, and gain practical experience by adjusting models and analyzing vibration sensor data in a real-world IoT example.

    #Machine Learning #Signal Processing
    Visit icon

    Gain comprehensive knowledge of ML pipelines, model persistence, Spark applications, data engineering, and hands-on experience with Spark SQL and SparkML for regression, classification, and clustering.

    #Machine Learning #Machine Learning Pipelines
    Visit icon

    This course explores enterprise machine learning applications, assesses the viability of ML use cases, and addresses the prerequisites, data characteristics, and critical factors for developing and managing ML models.

    #Machine Learning #Google Cloud
    Visit icon

    Learn to use Databricks and MLlib for creating and advancing machine learning models with Spark.

    #Machine Learning #MLlib
    Visit icon

    Acquire the ability to create custom Datasets and DataLoaders in PyTorch and train a ResNet-18 model for image classification.

    #Machine Learning #Deep Learning
    Visit icon

    Acquire the ability to differentiate between static and dynamic training and inference, manage model dependencies, establish distributed training for defect tolerance and replication, and generate exportable models.

    #Machine Learning #Google Cloud
    Visit icon

    Explore healthcare data mining methods, theoretical foundations of key techniques, selection criteria, and practical applications with emphasis on data cleansing, transformation, and modeling for real-world problem solving.

    #Machine Learning #Data Mining
    Visit icon