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    Hyperparameter Optimization For Machine Learning

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    Hyperparameter Optimization For Machine Learning - Online Course Download
    Hyperparameter Optimization For Machine Learning - Online Course Download
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    What you'll learn :

    Hyperparameter tunning and why it matters
    Cross-validation and nested cross-validation
    Hyperparameter tunning with Grid and Random search
    Bayesian Optimisation
    Tree-Structured Parzen Estimators, Population Based Training and SMAC
    Hyperparameter tunning tools, i.e., Hyperopt, Optuna, Scikit-optimize, Keras Turner and others

    Requirements :

    Python programming, including knowledge of NumPy, Pandas and Scikit-learn
    Familiarity with basic machine learning algorithms, i.e., regression, support vector machines and nearest neighbours
    Familiarity with decision tree algorithms and Random Forests
    Familiarity with gradient boosting machines, i.e., xgboost, lightGBMs
    Understanding of machine learning model evaluation metrics
    Familiarity with Neuronal Networks

    Description :

    Welcome to Hyperparameter Optimization for Machine Learning. In this course, you will learn multiple techniques to select the best hyperparameters and improve the performance of your machine learning models.

    If you are regularly training machine learning models as a hobby or for your organization and want to improve the performance of your models, if you are keen to jump up in the leader board of a data science competition, or you simply want to learn more about how to tune hyperparameters of machine learning models, this course will show you how.

    We’ll take you step-by-step through engaging video tutorials and teach you everything you need to know about hyperparameter tuning. Throughout this comprehensive course, we cover almost every available approach to optimize hyperparameters, discussing their rationale, their advantages and shortcomings, the considerations to have when using the technique and their implementation in Python.

    Specifically, you will learn:

    What hyperparameters are and why tuning matters
    The use of cross-validation and nested cross-validation for optimization
    Grid search and Random search for hyperparameters
    Bayesian Optimization
    Tree-structured Parzen estimators
    SMAC, Population Based Optimization and other SMBO algorithms
    How to implement these techniques with available open source packages including Hyperopt, Optuna, Scikit-optimize, Keras Turner and others.

    By the end of the course, you will be able to decide which approach you would like to follow and carry it out with available open-source libraries.

    This comprehensive machine learning course includes over 50 lectures spanning about 8 hours of video, and ALL topics include hands-on Python code examples which you can use for reference and for practice, and re-use in your own projects.

    So what are you waiting for? Enroll today, learn how to tune the hyperparameters of your models and build better machine learning models.

    Who this course is for :

    Students who want to know more about hyperparameter optimization algorithms
    Students who want to understand advanced techniques for hyperparameter optimization
    Students who want to learn to use multiple open source libraries for hyperparameter tuning
    Students interested in building better performing machine learning models
    Students interested in participating in data science competitions
    Students seeking to expand their breadth of knowledge on machine learning

    Course Size Details :

    9.5 hours on-demand video
    12 articles
    Full lifetime access
    Access on mobile and TV
    Certificate of completion

    View Demo

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