The following are some of the common challenges that you will face while running your machine learning application:
- Data quality: Data from sources is, most of the time, not suitable for machine learning. It has to be cleaned or checked for data quality first. Data has to be in the format that is suitable for the machine learning processes that you want to run. One such example would be removing nulls. The popular machine learning algorithm Random Forest does not support nulls.
- Data scaling: Sometimes, your data is comprised of attributes that vary in magnitude or scale. So, to prevent machine learning algorithms from being unbiased to re-scaling, under-scaled or over-scaled, attributes of the same scale is helpful. This helps machine learning optimization algorithms like gradient descent a great deal. Algorithms that iteratively weigh inputs, like regression and neural networks, or algorithms that are based on distance measures, like k-nearest neighbors...
