Machine learn­ing tack­les data-relat­ed problems

Machine learn­ing (ML) is about learn­ing to do bet­ter in the future based on past expe­ri­ence. The empha­sis of machine learn­ing is on auto­mat­ic meth­ods. In oth­er words, the goal is to devise learn­ing algo­rithms that do the learn­ing auto­mat­i­cal­ly with­out human inter­ven­tion or assis­tance. Islet­ter & soft­ware devel­op­er Sámuel Zeke gives exam­ples of fore­cast­ing and oth­er uses of ML algorithms.

Machine learn­ing uses pro­grammed algo­rithms that receive and analyse input data to pre­dict out­put val­ues with­in an accept­able range. As new data is fed to these algo­rithms, they learn and opti­mise their oper­a­tions to improve per­for­mance, devel­op­ing ‘intel­li­gence’ over time.

For more exam­ples, read about how Über applies arti­fi­cial intel­li­gence: https://​eng​.uber​.com/​a​p​p​l​y​i​n​g​-​a​r​t​i​f​i​c​i​a​l​-​i​n​t​e​l​l​i​g​e​n​c​e​-​a​t​-​u​b​er/

#Machine­Learn­ing #ML #Algo­rithms #Dis­rup­tiveTech­nolo­gies #Emerg­ingTech­nolo­gies