Waleed The IT Guy

Machine learning (ML) is a hot topic nowadays. Everyone speaks about the new programming paradigm, models are implemented in very different domains, more and more startups are relying mainly on ML.  At the same time, machine learning is a complex field having several different dimensions. Sometimes even experienced technical specialists can hardly imagine the whole ML universe and their place in this universe. Lots of people are just curious about ML and are not deeply immersed in the subject. For those, it is also important to understand the structure of machine learning.  Visualization of concepts is one of the best ways to ensure correct understanding and memorization of the particular domains. This is exactly what mindmaps help to do. We have prepared the machine learning mindmap that we hope will be useful for you. Note that machine learning is a subfield of data science, that is the more wide area. For those who are interested in data science, we can recommend another ou...

Data science development is very different from software development, and getting the two to mesh is sometimes like trying to cobble together Tinker Toys with Lincoln Logs.     One data science expert once described the differences as: Software development is “Measure twice; cut once,” while Data Science is “Cut, cut, cut!” The methodologies and processes that support successful software development do not work for data science projects according to one simple observation:   software development  knows , with 100% assurance, the expected outcomes, while data science – through data exploration and hypothesis testing, failing and learning –  discovers those outcomes. First introduced in the blog “ What's The Difference Between BI Analyst and Data Scientist ?”, the Data Science Engagement methodology in Figure 1 supports the rapid exploration, rapid testing, and continuous learning Data Science “Scientific Method [1] ”. Figure   1 :    Data Science ...