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This is the most comprehensive Full Stack Data Science program available that covers all steps of the Data Science process, from Data Integration, Data Manipulation, Descriptive Analytics and Visualization to Statistical Analysis, Predictive Analytics, and Machine Learning models using R, Python, Tableau, Tensor Flow, and Keras.
Get your development environment set up correctly with instructor Bruce Van Horn's step-by-step guidance, and explore PyCharm's first-rate text editing tools. Learn how to improve your code quality with Lens Mode and Intentions, refactor and debug code, and perform unit testing with the PyCharm test runner.
In this practical, hands-on course, learn how to use Python for data preparation, data munging, data visualization, and predictive analytics. Instructor Lillian Pierson, P.E. covers the essential Python methods for preparing, cleaning, reformatting, and visualizing your data for use in analytics and data science.
Programming is learned in small bits. You build on basic concepts. You transfer the knowledge you already have to the next language. Lunch Break Lessons teaches one of the most popular programming languages for data analysis and reporting'in short lessons that expand on what existing programmers already know.
Start by learning to manage packages and structure data for visualizations with the tidyverse and the pipe operator. Then there is an important question: Which library should you choose? The course introduces five popular options: Leaflet, Plotly, Highcharter, visNetwork, and DataTables (DT).
In this liveVideo course, machine learning expert Oliver Zeigermann teaches you the basics of deep learning. With Oliver Zeigermann's crystal-clear video instruction and the hands-on exercises in this video course, you'll get started in deep learning using open-source Python-friendly tools like scikit-learn and Keras, and TensorFlow 2.0 (soon to be officially released with exciting new updates!).
Keras in Motion teaches you to build neural-network models for real-world data problems using Python and Keras. In over two hours of hands-on, practical video lessons, you'll apply Keras to common machine learning scenarios, ranging from regression and classification to implementing Autoencoders and applying transfer learning.