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    Python for Data Science Essential Training Part 2

    Posted By: IrGens
    Python for Data Science Essential Training Part 2

    Python for Data Science Essential Training Part 2
    .MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 3h 44m | 426 MB
    Instructor: Lillian Pierson, P.E.

    Python for Data Science Essential Training is one of the most popular data science courses at LinkedIn Learning. It has now been updated and expanded to two parts—for even more hands-on experience with Python. In this course, instructor Lillian Pierson takes you step by step through a practical data science project: building machine learning models that can generate predictions and recommendations and automate routine tasks. Along the way, she shows how to perform linear and logistic regression, use K-means and hierarchal clustering, identify relationships between variables, and use other machine learning tools such as neural networks and Bayesian models. You should walk away from this training with hands-on coding experience that you can quickly apply to your own data science projects.

    Topics include:

    Why use Python for data science
    Machine learning 101
    Linear regression
    Logistic regression
    Clustering models: K-means and hierarchal models
    Dimension reduction methods
    Association rules
    Ensembles methods
    Introduction to neural networks
    Decision tree models


    Python for Data Science Essential Training Part 2