Data Science

ML Algorithms: Supervised Learning Tip to Tail

(0 reviews)
Share icon
Coursera With GroupifyAI

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.

Key AI Functions:

Machine Learning,Supervised Learning,Decision Tree

Description for ML Algorithms: Supervised Learning Tip to Tail

  • The course delves into the fundamentals of a machine learning project, with a particular emphasis on support vector machines, decision trees, and k-nearest neighbors for the purpose of conducting real-world case studies.
  • It instructs on the comparison of data preparation procedures and the resolution of production challenges in the context of applied machine learning.
  • A fundamental comprehension of linear algebra and statistics, as well as a basic understanding of Python programming, are necessary.
  • This is the second course in the Applied Machine Learning Specialization, which is offered by the Alberta Machine Intelligence Institute and Coursera.
  • Level: Intermediate

    Certification Degree: Yes

    Languages the Course is Available: 22

    Offered by: On Coursera provided by Alberta Machine Intelligence Institute

    Duration: 9 hours (approximately)

    Schedule: Flexible

    Reviews for ML Algorithms: Supervised Learning Tip to Tail

    0 / 5

    from 0 reviews

    Ease of Use

    Ease of Customization

    Intuitive Interface

    Value for Money

    Support Team Responsiveness

    Alternative Tools for ML Algorithms: Supervised Learning Tip to Tail

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

    #Logistic Regression #Unsupervised Learning
    Visit icon

    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

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

    #Data Clustering Algorithms #Machine Learning
    Visit 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
    Visit 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
    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

    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
    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

    Learn through case studies, techniques, challenges, and objectives to master classification tasks, techniques, and metrics in Python for effective machine learning on various datasets.

    #Logistic Regression #Statistical Classification
    Visit icon