How to Build a Machine Learning System

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Build a Machine Learning System Humans have always dreamed of emulating their own abilities in an invention. This all started with creating intelligent computer programs, developing still into smarter robots. This creation of robots and intelligent systems, involves using algorithms that can learn from data (examples), those are called Machine Learning algorithms. In 1959, Arthur Samuel defined Machine Learning (ML) as a "Field of study that gives computers the ability to learn without being explicitly programmed". [1] An example of widely used Machine Learning task is Spam Filtering, this task’s goal is to produce a computer program that labels incoming unseen messages as either spam or not. Topic modeling is another example, in which a program is given a list of human language documents and is tasked to find out which documents cover similar topics. A more difficult example of a ML task is driving a vehicle automatically; in this task the computer program interacts with a dynamic environment in which it must perform a certain goal. Creating a Machine Learning system typically requires five main steps, which are explained below. First and foremost, you should start with collecting relevant data for the problem you want to solve. For an online business user click, search queries, and browsing information should all be captured and saved in the database. In manufacturing, log data captures machine status and activities. Such data is used to produce maintenance schedules and predict which parts will require replacement. After data collection, a data preprocessing stage is performed. This preprocessing is performed because the data used should describe factors, attributes, or features of an observation. A simple first step in looking at the collected data is data cleaning, which involves mainly finding missing values. For example, perhaps a person filling out an

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