Machine learning and big data are two of the most significant technological trends of our time, and they are dramatically altering the business landscape. The science behind these advanced technologies is fascinating, complex, and constantly evolving.

At its core, machine learning is a method of data analysis that automates analytical model building. It’s a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. It involves algorithms that improve their performance at tasks over time through experience.

The process begins by feeding the machine learning algorithm with training data to learn from. This initial set consists of input examples that help shape or train the model. Once trained adequately, it can then process new data to predict and output information accurately.

In contrast, big data refers to extremely large datasets that may be analyzed computationally to reveal patterns, trends, and associations relating to human behavior and interactions. Big Data isn’t just about size though; it also concerns the velocity at which this data is generated as well as its variety – including structured numerical data in traditional databases as well as unstructured text documents or social media posts.

When combined together, machine learning and big data provide an incredibly powerful tool for businesses across various sectors. Machine learning algorithms can analyze more complex and larger volumes of data compared to humans without fatigue or bias. They can identify hidden patterns within this vast amount of information quickly – something beyond human capability due to sheer volume alone.

Underpinning all these processes is computational power – computers capable enough to handle such massive amounts of information swiftly while running intricate algorithms simultaneously. As technology advances further in terms of processing speed capabilities so does our ability exploit these tools effectively.

However fascinating this technology might be though it’s not without challenges – privacy concerns being one major issue with handling such enormous amounts of personal information for instance; ensuring accuracy in predictions another since incorrectly interpreted results could lead disastrous consequences especially when used decision-making purposes.

In conclusion, the science behind machine learning and big data is a complex interplay of algorithms, computational power, vast amounts of data, and sophisticated analysis techniques. The potential these technologies hold for transforming business operations, decision-making processes and our understanding of the world around us is immense. However, as we continue to push the boundaries of what’s possible with machine learning and big data analytics, we must also be mindful of the ethical implications and challenges that come with it.