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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-with-python-av/
课程评论：没有评论
课程名称：数据科学认证 课程概述：您是否想学习如何使用Python进行数据科学和机器学习？如果是的话，这门课程非常适合您！该课程旨在帮助学生和专业人士获取数据科学和机器学习的实用知识和技能，涵盖数据分析、可视化和机器学习所需的各种主题。 课程内容包括数据科学和机器学习的概念和术语概述。学生将进行Python编程的速成课程，为数据科学打下坚实的基础。他们将学习使用Numpy和pandas进行数据分析，使用Matplotlib和seaborn进行数据可视化。课程还涵盖数据预处理、清理、编码、缩放和数据拆分等机器学习的重要步骤。 此外，课程介绍多种机器学习技术，包括监督学习、无监督学习和强化学习，以及各种模型，如线性回归、逻辑回归、朴素贝叶斯、k近邻算法、决策树、随机森林、支持向量机和k均值聚类。学生将通过scikit-learn获得实操训练，学习如何训练、评估、调优和验证模型。 课程还将介绍自然语言处理技术，包括预处理、句子分割、标记化、词性标注、停用词移除、词形还原和频率分析，并通过可视化展示NLP数据中的依赖关系。在课程的最后一周，学生将完成一个最终项目并参加认证考试。
Are you interested in learning data science and machine learning with Python? If so, this course is for you! Designed for students and professionals who want to acquire practical knowledge and skills in data science and machine learning using Python, this course covers various topics that are essential for building a strong foundation in data analysis, visualisation, and machine learning. The course covers various essential topics such as an overview of data science and machine learning concepts and terminology. Students will follow a crash course on Python Programming for a strong foundation for Data Science. They will learn about data analysis using Numpy and pandas, and data visualization using Matplotlib and seaborn. Students will also learn about data preprocessing, cleaning, encoding, scaling, and splitting for machine learning. The course covers a range of machine learning techniques, including supervised, unsupervised, and reinforcement learning, and various models such as linear regression, logistics regression, naives bayes, k-nearest neighbours, decision trees and random forests, support vector machines, and k-means clustering. In addition, students will get hands-on training with scikit-learn to train, evaluate, tune, and validate models. They will also learn about natural language processing techniques, including pre-processing, sentence segmentation, tokenization, POS tagging, stop word removal, lemmatization, and frequency analysis, and visualizing dependencies in NLP data. The final week of the course involves working on a final project and taking certification exams.
