Machine learning for data science (https://campnewsmedia.com/machine-learning-for-data-science/) serves as the backbone of the entire field. Without machine learning, you can’t build the analysis models that help you gain new insights from your data, or create the algorithms that help you make smart decisions based on data-driven insights.
For this reason, every data scientist needs to have at least some knowledge about machine learning, even if he or she does not actively use it in every project he or she works on.

Machine learning is a type of artificial intelligence that allows computer software to become more accurate in predicting outcomes without being explicitly programmed. Instead of relying on rules-based programming, it learns from data and makes predictions based on what it has learned. When machines learn, they get better over time.

Machine learning is used in countless applications, from deciding which movie you might want to watch next to determining your credit score. In fact, machine learning has been around since at least 1950, when psychologist Frank Rosenblatt created one of the first artificial neural networks (ANNs).
The term machine learning was coined by Arthur Samuel in 1959. ANNs are modeled after our own brain cells and how they connect with each other through synapses. These artificial connections allow us to recognize patterns and make decisions, two essential components of machine learning.
Today’s ANNs are vastly different than those created decades ago; instead of mimicking neurons, today’s ANNs can process vast amounts of information quickly using powerful computers. Continue Reading in CampNews Media (https://campnewsmedia.com/)