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

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