Python has become the go-to tool for data scientists looking to build predictive models. This course covers the theoretical constructs of predictive analytics and teaches you how to use sci-kit learn, the main tool in Python for predictive analytics. You'll use real-world datasets to build a variety of models and will learn all the different stages to building predictive analytics models in Python.
Python has become one of any data scientist's favorite tools for doing Predictive Analytics. In this hands-on course, you will learn how to build predictive models with Python. During the course, we will talk about the most important theoretical concepts that are essential when building predictive models for real-world problems. The main tool used in this course is scikit -learn, which is recognized as a great tool: it has a great variety of models, many useful routines, and a consistent interface that makes it easy to use. All the topics are taught using practical examples and throughout the course, we build many models using real-world datasets. By the end of this course, you will learn the various techniques in making predictions about bankruptcy and identifying spam text messages and then use our knowledge to create a credit card using a linear model for classification along with logistic regression. Style and Approach: This course introduces the main concepts, techniques, and best practices for doing Predictive Analytics with Python. Using an example-based approach, it covers all the stages in the process of building predictive models with Python. By the end of the course you will be able to build Predictive Analytics models using real-world data.
The course is designed for Data analysts or data scientists interested in learning how to use Python to perform Predictive Analytics as well as Business analysts/business Intelligence experts who would like to go from descriptive analysis to predictive analysis. Software engineers and developers interested in producing predictions via Python will also benefit from the course. Knowledge of the Python programming language is assumed. Basic familiarity with Python's Data Science Stack would be useful, although a brief review is given. Familiarity with basic mathematics and statistical concepts is also advantageous to take full advantage of this course.