Author: Zen and the Art of Computer Programming
1 Introduction
First, the meaning of this question needs to be clarified. As a computer science graduate, have you taken your first steps towards becoming a good machine learning engineer? Or have you heard how fascinating the career is, but never had the chance to try it out? If you haven't decided whether to try this industry, don't worry, by reading this article, you can quickly understand the knowledge and skills required to become an excellent machine learning engineer, as well as access to resources in this field. Second, regarding the scope of this article, the author hopes that it not only covers the general knowledge and skills for machine learning engineers, but also specifically introduces the latest developments, application scenarios and solutions, and shortcomings of some popular machine learning technologies. Therefore, this article will include the following: - Overview: introduce the basic concepts and development history of machine learning - In-depth understanding: master the core algorithms of machine learning, model parameter estimation methods, classification, regression and other skills - Model performance optimization: master the model Parameter adjustment skills, improve model training effect and improve model generalization ability - Deployment and monitoring: understand the requirements for deploying online machine learning models, and master the methods of monitoring model performance - Other concerns: summarize existing technical bottlenecks and look forward to future development At the end of the direction, the author will provide a series of related resources for readers' reference, including papers, open source libraries, courses, books, etc. # 2. Overview ## 2.1 What is machine learning? Machine Learning (English: Machine Learning) is a discipline that studies how to enable computers to achieve certain prediction or decision-making functions through data and calculations. Machine learning is a type of technology that automatically analyzes and learns from data, and uses the acquired knowledge to predict and make decisions on new input data. ### 2.1.1 Algorithms and models Machine learning algorithms can be simply understood as "rules" or "methods", which are used to generate models based on data sets. For example, the K-nearest neighbor method is one of the simplest machine learning algorithms, which judges the appropriateness of new input data based on known data samples.