Mirrorsoft Technologies

  Machine Learning VS Deep Learning In the world of AI, it has been seen that people use the terms machine learning and deep machine learning often interchangeably. But that’s not the right thing to do! If you want to understand artificial intelligence better, you will have to be able to differentiate between these two vital ways in which computer systems learn. Thankfully, the concepts of machine learning and deep learning are not as complex as they might seem. click here:  learn Machine Learning from Expert Put simply, machine learning is a subset of  artificial intelligence . Whereas, deep learning is a subset of machine learning. So, both the concepts are interconnected in a way, but they are for sure not interchangeable. Now, let’s take a look at the most prominent factors on the basis of which they are distinguished from each other.

  Machine Learning VS Deep Learning In the world of AI, it has been seen that people use the terms machine learning and deep machine learning often interchangeably. But that’s not the right thing to do! If you want to understand artificial intelligence better, you will have to be able to differentiate between these two vital ways in which computer systems learn. Thankfully, the concepts of machine learning and deep learning are not as complex as they might seem. Put simply, machine learning is a subset of  artificial intelligence . Whereas, deep learning is a subset of machine learning. So, both the concepts are interconnected in a way, but they are for sure not interchangeable. Now, let’s take a look at the most prominent factors on the basis of which they are distinguished from each other.

  Statistics has proven to be the biggest game changer in the context of a business in the 21st century, leading to the boom of the new oil, that is “Data”. Through this blog, we aim to provide a definitive understanding to the reader on how the process of Statistical Analysis for Data Science can be done on an actual business use case. Let’s get started : Data can be analysed to get valuable insights, but when analysis isn’t done, data is just a bunch of numbers that wouldn’t make any sense.  Click here:  learn from Expert Now! According to  Croxton and Cowden, Statistics is a Science of Collection, Presentation, Analysis and Interpretation of any numerical data. A few examples include: Route Optimisation in Airlines Industry ROI Prediction of a company Stock Market Share Price Prediction Predictive Maintenance in Manufacturing For any data set, statistical analysis for Data Science can be done according to the six points as shown below. They form the skeleton of st...