Predicting CTR with Machine Learning in Python
Learn how to predict click-through rates on ads and implement basic machine learning models in Python so that you can see how to better optimize your ads.
Follow short videos led by expert instructors and then practice what you’ve learned with interactive exercises in your browser.
Learn how to predict click-through rates on ads and implement basic machine learning models in Python so that you can see how to better optimize your ads.
Apply fundamental concepts in network analysis to large real-world datasets in 4 different case studies.
Learn how to visualize big data in R using ggplot2 and trelliscopejs.
Learn statistical tests for identifying outliers and how to use sophisticated anomaly scoring algorithms.
Learn to create animated graphics and linked views entirely in R with plotly.
Master the essential skills of data manipulation in Julia. Learn how to inspect, transform, group, and visualize DataFrames using real-world datasets.
Learn mixture models: a convenient and formal statistical framework for probabilistic clustering and classification.
Continue learning with purrr to create robust, clean, and easy to maintain iterative code.
Use C++ to dramatically boost the performance of your R code.
Learn to predict labels of nodes in networks using network learning and by extracting descriptive features from the network
Learn how to write scalable code for working with big data in R using the bigmemory and iotools packages.
Create a go-to-market strategy with generative AI: target industries, generate leads, and optimize website keywords.
Build real-world applications with Python—practice using OOP and software engineering principles to write clean and maintainable code.
Test a chatbot that matches customers with ideal skincare products using your prompting skills for personalized results.
Analyze market dynamics and craft a strategic entry plan for an EV manufacturer using generative AI.