Python and R Intermediate (PYR102)
This PYR102 course is designed to learn basic AI application. This course will provide you with training environment to get used to basic AI concept and methodology such as classification, clustering, dimensionality reduction, regression modeling, supervised learning and unsupervised learning in Machine Learning, Deep learning, and Neural Network utilizing Python and R software work environment. Python and R software are very popular in AI application and there are lots of advantages for your learning and skill enhancement. One of the key hot trends now a day is about increasing data volumes, computer speed, and Internet thanks to IT innovation and now is 3rd AI innovation era. You will see more AI application in all business area and the knowledge and skill for AI are expected. This course objective is to learn concepts, method, and application to be familiar with basic AI planning, algorism selections, learning techniques, and modeling through Python (with Anaconda, Spyder, and Jupyter) and R training business cases.
Masatoshi Hirakata has more than 20 years of leadership experience in various business units' functions and management positions in Asia Pacific and international IT business area. He earned a BA in Electrical & Electronics Engineering from Sophia University and an MA in Economics from Temple University Japan. He has been teaching in the Continuation Education program at Temple University for more than 10 years. Recently he started a series of AI and statistics courses using Python and R. He also enjoys Skiing for many years and has recently become interested in Skiing as a lifetime sport and its relationship with preventive medicine. He studied physical conditioning, fitness methodology, and skiing technical reference at Canadian Sport Business Academy. He holds SAJ Skiing class 1 certificate, Canadian Skiing Instructor Association Level 2 certificate, and Avalanche Skill Training 1 certificate from Avalanche Canada.
This course applies to the following certificate(s):
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