Machine learning means learning from data;
AI is a buzzword. Machine learning lives up to the hype: there are an incredible number of problems that you can solve by providing the right training data to the right learning algorithms. Call it AI if that helps you sell it, but know that AI is a buzzword that can mean whatever people want it to mean.
Machine learning is about data and algorithms, but mostly data.
Unlike artificial intelligence, machine learning mostly depends on huge amounts of data and its analysis. Despite the fact that numerous IT specialists are constantly working in attempts to make algorithms of machine learning more sophisticated and accurate, the key factor which determines the success of this process is the amount of available data.
Unless you have a lot of data, you should stick to simple models.
Machine learning trains a model from patterns in your data, exploring a space of possible models defined by parameters. If your parameter space is too big, you’ll overfit to your training data and train a model that doesn’t generalize beyond it. A detailed explanation requires more math, but as a rule you should keep your models as simple as possible.
Machine learning can only be as good as the data you use to train it.
The effectiveness of machine learning is dependent on the quality of the data that was used to train it. Obviously, if the provided data is limited, machine learning can overlook some patterns, thus it benefits from the diversity of the given material.
Machine learning only works if your training data is representative.
Just as a fund prospectus warns that “past performance is no guarantee of future results”, machine learning should warn that it’s only guaranteed to work for data generated by the same distribution that generated its training data. Be vigilant of skews between training data and production data, and retrain your models frequently so they don’t become stale.