Machine Learning For Energy Forecast

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


Homepage

https://www.udemy.com/course/machine-learning-for-energy/

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