performance (pan ml algorithm is supposed to perform task and gain experience with the passage of time the measure which tells whether ml algorithm is performing as per expectation or not is its performance (pp is basically quantitative metric that tells how model is performing the tasktusing its experiencee there are many metrics that help to understand the ml performancesuch as accuracy scoref scoreconfusion matrixprecisionrecallsensitivity etc challenges in machines learning while machine learning is rapidly evolvingmaking significant strides with cybersecurity and autonomous carsthis segment of ai as whole still has long way to go the reason behind is that ml has not been able to overcome number of challenges the challenges that ml is facing currently arequality of datahaving good-quality data for ml algorithms is one of the biggest challenges use of low-quality data leads to the problems related to data preprocessing and feature extraction time-consuming taskanother challenge faced by ml models is the consumption of time especially for data acquisitionfeature extraction and retrieval lack of specialist personsas ml technology is still in its infancy stageavailability of expert resources is tough job no clear objective for formulating business problemshaving no clear objective and well-defined goal for business problems is another key challenge for ml because this technology is not that mature yet issue of overfitting underfittingif the model is overfitting or underfittingit cannot be represented well for the problem curse of dimensionalityanother challenge ml model faces is too many features of data points this can be real hindrance difficulty in deploymentcomplexity of the ml model makes it quite difficult to be deployed in real life applications of machines learning machine learning is the most rapidly growing technology and according to researchers we are in the golden year of ai and ml it is used to solve many real-world complex problems which cannot be solved with traditional approach following are some real-world applications of mlemotion analysis sentiment analysis error detection and prevention weather forecasting and prediction stock market analysis and forecasting speech synthesis speech recognition