Advanced ML Algorithms

Advanced ML Algorithms

(0 reviews)
Share icon

The course emphasizes the utilization of regularization to ensure the robustness of models, ensemble methods to enhance accuracy, and hyperparameters and feature engineering to optimize models for real-world challenges.

Key AI Functions:Bagging and Boosting Algorithms,Model Selection,Regularization,hyperparameter tuning

Description for Advanced ML Algorithms

Features of Course

  • Utilize regularization methods to improve the robustness and efficacy of the model.
  • Enhance predictive accuracy by employing ensemble methods, including bagging and boosting.
  • Refine models for real-world challenges by implementing hyperparameter optimization and feature engineering.
  • Expand your predictive arsenal by integrating a variety of models to achieve superior predictions.
  • Level: Beginner

    Certification Degree: Yes

    Languages the Course is Available: 21

    Offered by: On Coursera provided by Fractal Analytics

    Duration: 20 hours (approximately)

    Schedule: Flexible

    Reviews for Advanced ML Algorithms

    0 / 5

    from 0 reviews

    Ease of Use

    Ease of Customization

    Intuitive Interface

    Value for Money

    Support Team Responsiveness

    Alternative Tools for Advanced ML Algorithms

    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

    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

    Learn to clean, prepare, analyze, and manipulate data with Python, utilize libraries for exploratory data analysis, and develop regression models for prediction and decision-making using scikit-learn.

    #Model Selection #Data Analysis
    icon

    Learn Python, analyze and visualize data, and apply your skills to data science and machine learning with a practical assignment to acquire hands-on skills for a career in data science.

    #Model Selection #Data Analysis
    icon