Big data, analytics, the Internet of Things, cloud technology... In the last few years, without these terms you can't get involved in discussions and conversations about technology. They have become the main players in technology, affecting all aspects of business. The change seems to be happening at an extremely fast rate and there is no sign of slowing down. Today, the only constant in technology is the change itself. Constant change requires constant innovation, so more new technologies need to be introduced. One of the new technologies that enter the topic of technology is machine learning. Gartner pointed out that machine learning is one of the top ten technology trends in 2016. There is no doubt that this is a hot topic. Everything is new and new What I found interesting is that the basic principles of machine learning can be traced back to artificial intelligence research in the 1970s and early 1980s. The work at the time was limited by computer performance and data utilization. Breaking through these constraints is the key to the dramatic progress in machine learning in recent years. Computational cycles and data utilization levels were unthinkable decades ago. The goal of machine learning sounds simple: Provide the system's ability to learn based on information. Although it sounds simple, it is challenging the classic software engineering. Most of the "hard-coded" software development systems we are familiar with are based on planned and expected user data interactions. The standard "if – then – else" model. The algorithm requirements for artificial intelligence/machine learning are more complicated. They need to allow the system to form its own input-based analysis mode. These patterns are constantly changing based on the information provided. Based on the data and these patterns, the behavior is determined. As you learned from the description, this will lead to very uncertain behavior. The system will analyze, understand, and react based on the information provided, adjust behavior based on more information, and then provide feedback. The analysis and behavior are constantly changing and have been improved over time. Imagine a test that fits this system! (The subject to be discussed) You are enjoying the convenience of machine learning Do you have an account with Netflix? What about Amazon? Every time you log in, Netflix and Amazon provide you with a list of recommendations. Both companies have very complex, proprietary algorithms. Through these algorithms, you can analyze the huge information base that contains transactions with you and all other members. Based on this data, they built your expected behavioral model and presented a series of recommendations to you. Your response to these suggestions is also fed back into the algorithm, and the algorithm is continually adjusted to suit your behavioral patterns. What about your smartphone? Think about the complexity of the simple sentence "Mr. What is the weather like today?" First, the software needs to recognize your voice, accent, and the way you speak in order to determine what you actually say. If this is not very certain, the software asks for confirmation and it learns from your confirmation. Every time you use speech recognition, your phone will better understand what you said. Once you understand what you are saying, it will process the natural language into a meaningful language into the system. This in turn requires sophisticated algorithms to analyze the speech information, create models, and then execute instructions on its translation results. When parsing the voice, if it is not very sure, the software will promptly confirm. The model you use to speak and the context in which you use the language give feedback to the system. This is all data is working A recent article on TechCrunch, 'How startups compete with companies in the field of artificial intelligence and machine learning' John Melas-Kyriazi talks about data: ''We invest resources in training machine learning models that will create huge networks effect. 'I found a very appropriate metaphor. Complex algorithms and models are engines of machine learning, but without fuel, engine-data does not work well. One of my colleagues, John Williams, (CEO of CollaboraTIve ConsulTIng) has long liked to say, “This is all data is working.†It’s true in the world of machine learning. A good laminate structure can control the characteristic impedance of the printed circuit board, and the traces can form an easily controllable and predictable transmission line structure called an impedance plate. 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