Machine Learning (ML) Applications are one of the most interesting topics nowadays among the Artificial Intelligence (AI) research community, it can get a bit complicated for some of us, since we have no deep background or in-depth knowledge about such an important topic, but there are many easy to use tools to give us a chance to contribute to this interesting area of research with build-in, and ready to use tools, such as WEKA solution. We will introduce the solution, and most of its functionality in order to demonstrate a fair understanding of a common classification problem using different, well known, and widely used ML classification algorithms, and such an exercise will be done on a selected bioinformatic dataset. Then we will introduce a feature selection method for the features of the used dataset for different classifiers to evaluate the performance of the classifier with the selected features, and compare it with the original classification. If time permits, we will educate ourselves with some sensitivity analysis on selected classifiers' parameters in order to see if there is an enhancement of the classifier performance with respect to these parameters. I am not an expert in ML, but I can promise all of you, and by the end of these sessions, with some effort and dedication from your side, a reasonable publishable work can be produced by each one of you.
ะกะผะพััะธัะต ะฒะธะดะตะพ ๐๐๐๐ก๐ข๐ง๐ ๐๐๐๐ซ๐ง๐ข๐ง๐ ๐ข๐ญ ๐๐๐ง'๐ญ ๐ ๐๐ญ ๐๐ง๐ฒ ๐๐๐ฌ๐ข๐๐ซ ๐ญ๐ก๐๐ง ๐ญ๐ก๐ข๐ฌ! ะพะฝะปะฐะนะฝ ะฑะตะท ัะตะณะธัััะฐัะธะธ, ะดะปะธัะตะปัะฝะพัััั ัะฐัะพะฒ ะผะธะฝัั ัะตะบัะฝะด ะฒ ั ะพัะพัะตะผ ะบะฐัะตััะฒะต. ะญัะพ ะฒะธะดะตะพ ะดะพะฑะฐะฒะธะป ะฟะพะปัะทะพะฒะฐัะตะปั Artificial Intelligence and Data Analytics Lab 17 ะะพัะฑัั 2021, ะฝะต ะทะฐะฑัะดััะต ะฟะพะดะตะปะธัััั ะธะผ ัััะปะบะพะน ั ะดััะทััะผะธ ะธ ะทะฝะฐะบะพะผัะผะธ, ะฝะฐ ะฝะฐัะตะผ ัะฐะนัะต ะตะณะพ ะฟะพัะผะพััะตะปะธ 27 ัะฐะท ะธ ะพะฝะพ ะฟะพะฝัะฐะฒะธะปะพัั 0 ะปัะดัะผ.