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Introduction to machine learning
Machine learning is an interdisciplinary subject that uses computers to simulate or realize human learning behavior, and improves its own performance by continuously acquiring new knowledge and skills and reorganizing existing knowledge structures. Machine learning involves multiple disciplines, such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory, etc.
The main task of machine learning is to guide computers to learn from data and then use experience to improve their own performance, without explicit programming. Machine learning algorithms are continuously trained to discover patterns and correlations from large data sets and then make the best decisions and predictions based on the data analysis results. As more data becomes available, machine learning applications will become more accurate. Machine learning technology is now used in a wide range of applications, such as home life, shopping, entertainment media, and health care.
Machine learning and artificial intelligence are inseparable, and machine learning is a subset of artificial intelligence. Artificial intelligence is based on data processing to make decisions and predictions. Through machine learning algorithms, artificial intelligence can not only process data, but also use this data to learn and become smarter without the need for additional programming. Artificial intelligence is the parent set that encompasses all subsets of machine learning. Branches of machine learning include deep learning and neural networks, which are important components of artificial intelligence.
The working principle of machine learning is to continuously improve the model through training and optimization algorithms, so that the model can better adapt to new data and new situations. This process usually includes steps such as data preprocessing, feature extraction, model training, evaluation and adjustment. Machine learning has a wide range of applications, such as speech recognition, image recognition, natural language processing, recommendation systems, etc.
"Python Machine Learning Project Practice" book sharing
"Python Machine Learning Project Practice" leads everyone to master key machine learning concepts in the process of building actual projects! Using machine learning, we can complete tasks such as customer behavior analysis, price trend prediction, and risk assessment. Mastering machine learning requires quality examples, clear explanations, and lots of practice. "Python Machine Learning Project Practice" fully meets these three points!
"Python Machine Learning Project Practice" shows realistic and practical machine learning scenarios, and comprehensively and clearly introduces the key concepts of machine learning. In the process of learning "Python Machine Learning Project Practice", readers will complete some fascinating projects, such as using linear regression to predict car prices, deploying customer churn prediction services, etc. Readers will go beyond algorithms and learn to deploy machine learning applications on serverless systems and use important technologies such as Kubernetes and Kubeflow serving models. Everyone is studying hard, hands-on, and enjoying the fun of mastering machine learning skills!
main content:
- Collect and clean data for training models
- Use popular Python tools including NumPy, Scikit-Learn, and TensorFlow
- Deploy machine learning models into production
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