Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This course provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. Hands-on Predictive Analytics with Python is a three-day, hands-on course that guides students through a step-by-step approach to defining problems and identifying relevant data. Students will learn how to perform data preparation, explore and visualize relationships, as well as build models, tune, evaluate, and deploy models. Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seabor, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics.

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* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

Working in a hands-on learning environment, guided by our expert team, attendees will learn to
Understand the main concepts and principles of predictive analytics
Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects
Explore advanced predictive modeling algorithms w with an emphasis on theory with intuitive explanations
Learn to deploy a predictive model's results as an interactive application
Learn about the stages involved in producing complete predictive analytics solutions
Understand how to define a problem, propose a solution, and prepare a dataset
Use visualizations to explore relationships and gain insights into the dataset
Learn to build regression and classification models using scikit-learn
Use Keras to build powerful neural network models that produce accurate predictions
Learn to serve a model's predictions as a web application

Course Info

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Cost: $ 2,195

Length: 3.0 days (24 hours)

Level:

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