Python data mining and machine learning

In recent years, the Python programming language has been favored by more and more scientific researchers and continues to win the championship in many programming language rankings. At the same time, with the rapid development of deep learning, artificial intelligence technology is increasingly used in various fields. Machine learning is the foundation of artificial intelligence. Therefore, mastering the working principles of commonly used machine learning algorithms and being able to skillfully use Python to build actual machine learning models are the prerequisite and foundation for carrying out artificial intelligence-related research. Therefore, China Science and Technology Information Environment has launched a new Python data mining and machine learning course, tailor-made for people in various fields, allowing you to learn Python programming and machine learning theory and code implementation methods, from "basic programming → machine learning → "Code Implementation" is mastered step by step.

Peel away the cocoon and analyze the experience and programming skills needed to apply machine learning in a simple and easy-to-understand manner. Through practical cases, we introduce how to refine innovation points and how to publish high-level papers and other related experiences. Master the basic knowledge and skills of Python programming, feature engineering (data cleaning, variable dimensionality reduction, feature selection, group optimization algorithm), regression fitting (linear regression, BP neural network, extreme learning machine), classification recognition (KNN, Bayeux) Basic principles of classification, support vector machine, decision tree, random forest, AdaBoost, XGBoost and LightGBM, etc.), cluster analysis (K-means, DBSCAN, hierarchical clustering), association analysis (association rules, collaborative filtering, Apriori algorithm) And Python code implementation method.

Yu Lei (Associate Professor): Mainly engaged in MATLAB programming, machine learning and data mining, data visualization and software development, physiological system modeling and simulation, and biomedical signal processing. He has rich practical application experience. He is the editor-in-chief of "30 Cases of MATLAB Intelligent Algorithms" "Analysis" and "MATLAB Neural Network 43 Case Analysis" related works. Has published many high-level international academic research papers.

Original link: Python data mining and machine learning

Python data mining and machine learning

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