Description
In this course, you will learn what hyperparameters are, what Genetic Algorithm is, and what hyperparameter optimization is. In this course, you will apply Genetic Algorithm to optimize the performance of Support Vector Machines (SVMs) and Multilayer Perceptron Neural Networks (MLP NNs). It is referred to as hyperparameter tuning or parameter tuning. You will also learn how to do feature selection using Genetic Algorithm. Hyperparameter optimization will be done on two datasets: A regression dataset for the prediction of cooling and heating loads of buildingsA classification dataset regarding the classification of emails into spam and non-spamThe SVM and MLP will be applied on the datasets without optimization and compare their results to after their optimizationFeature Selection will be done on one dataset: Classification of benign tumors from malignant tumors in a breast cancer datasetBy the end of this course, you will have learnt how to code Genetic Algorithm in Python and how to optimize your machine learning algorithms for maximum performance. You would have also learnt how to apply Genetic Algorithm for feature selection. To sum up: You will learn what hyperparameters are (sometimes referred to as parameters, though different)You will learn Genetic AlgorithmYou will use Genetic Algorithm to optimize the performance of your machine learning algorithmsMaximize your model's accuracy and predictive abilitiesOptimize the performance of SVMs and MLP Neural NetworksApply feature selection to extract the features that are relevant to the predicted outputGet the best out of your machine learning modelRemove redundant features, which in return will reduce the time and complexity of your modelUnderstand what are the features that have a relationship to the output and which do notYou do not need to have a lot of knowledge and experience in optimization or Python programming - it helps, but not a must to succeed in this course. This course will teach you how to optimize the functionality of your machine learning algorithmsWhere every single line of code is explained thoroughlyThe code is written in a simple manner that you will understand how things work and how to code Genetic Algorithm even with zero knowledge in PythonBasically, you can think of this as not only a course that teaches you how to optimize your machine learning model, but also Python programming! Please feel free to ask me any question! Don't like the course? Ask for a 30-day refund!! Real Testaments ->1) This is my second course with Dana. This course is a combination of Metaheuristic and machine learning. It gives a wide picture of machine learning hyperparameter optimization. I recommend taking this course if you know basics of machine learning and you want to solve some problems using ML. By applying the techniques of GA optimization, you will have better performance of ML. The codes provided in this course are very straightforward and easy to understand. The course deserves five stars because of the lecture contents and examples. The instructor knowledgeable about the topic and talented in programming. - Abdulaziz, 5 star rating2) An excellent course! Great for anyone interested in fine-tuning their machine-learning models. I really enjoyed the from scratch implementations and how well they are explained. These implementations from scratch help one understand the theory very well. An interesting thing to point out is that this course uses Metaheustistics to optimise machine-learning. However, you can use machine-learning classifiers to help your Metaheuristic predict good or bad regions. - Dylan, 5 star rating3) Very helpful, for application of optimization algorithm to optimize ML algorithm parameters and got to do this using python, wonderful. - Erigits, 5 star rating4) well explained course. The topic is not an easy one but to date the explanations have been clear. The course has an interesting spreadsheet project. - Martin, 5 star rating5) Thank you very much for this awesome course. Lots of new things learn from this course. - Md. Mahmudul, 5 star rating
