Published 11/2022 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 645.65 MB | Duration: 1h 18m Presenting Linear Regression for Forecasting on Energy Datasets
What you'll learn How to actually use Machine Learning on Energy Datasets Clarifying key concepts about Machine Learning models Specialized analysis on all stages - starting with preprocessing until forecasts Theoretical foundations along with practical explanations Part of the giannelos official certificate for high-tech projects. Requirements The only prerequisite is to take the first course of the program , which is the course "Data Science Code that appears all the time at workplace". Description What is the course аbout:The course shows - step by step and in great detail - how to apply Machine Learning and specifically Linear Regression, on an energy dataset. Using this algorithm, we generate forecasts all the way to 2050. This requires fine tuning of all hyperparameters, including the selection of the degree of the polynomial. In depth sensitivity analyses are performed and demonstrate the importance of the forecasting error, which we evaluate using proxies and statistical measures.Who:I am a research fellow at Imperial College London, and I have been part of high-tech projects at the intersection of Academia & Industry for over 10 years, prior to, during & after my Ph.D. I am also the founder of the giannelos dot com program in data science.Doctor of Philosophy (Ph.D.) in Analytics & Mathematical Optimization applied to Energy Investments, from Imperial College London, and Master of Engineering (M. Eng.) in Power Systems and Economics. Important:Prerequisites: The course Data Science Code that appears all the time at Workplace.Every detail is explained, so that you won't have to search online, or guess. In the end, you will feel confident in your knowledge and skills. We start from scratch so that you do not need to have done any preparatory work in advance at all. Just follow what is shown on screen, because we go slowly and explain everything in detail. Overview Section 1: Preparing the data Lecture 1 Data Preprocessing Lecture 2 Polynomials Lecture 3 Splitting the dataset & defining targets Section 2: Fitting the LR models Lecture 4 Fitting Lecture 5 scaling Enterpreneurs,Economists,Quants,Members of the highly googled program,Investment Bankers,Academics, PhD Students, MSc Students, Undergrads,Postgraduate and PhD students.,Data Scientists,Energy professionals (investment planning, power system analysis),Software Engineers,Finance professionals
