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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-with-machine-learning-algorithm-using-python/
课程评论：没有评论
**课程名称：** 使用Python和机器学习算法的科学数据分析 **课程概述：** 本课程是开启数据科学和机器学习职业生涯的理想选择。课程全面覆盖了数据科学的生命周期，从Python基础概念到高级应用，并深入介绍了NumPy、SciPy、Pandas、Matplotlib、Seaborn和Plotly.py等核心库。 您将学习数据科学的项目启动步骤、案例分析，以及线性回归、逻辑回归、支持向量机（SVM）、K均值、KNN、朴素贝叶斯、决策树和随机森林等关键机器学习算法。课程还会讲解监督学习和无监督学习等机器学习类型，以及训练测试集划分、模型构建和评估等重要概念。 课程旨在通过Scikit-learn库的实际案例，帮助您深入理解和应用各种机器学习算法，从而掌握在数据密集型行业中取得成功所需的核心技能。data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process. **课程亮点：** * **Python编程基础与进阶：** 扎实的Python编程功底，为数据分析和机器学习打下坚实基础。 * **核心数据科学库：** 熟练掌握NumPy、SciPy、Pandas、Matplotlib、Seaborn和Plotly.py等数据科学常用库。 * **数据科学入门与项目实践：** 理解数据科学的价值，学习数据收集方法，掌握项目启动流程，并通过案例分析进行实践。 * **机器学习理论与算法：** 深入理解监督学习、无监督学习、模型训练与评估等概念，并掌握多种主流机器学习算法（如线性回归、逻辑回归、SVM、K均值、KNN、朴素贝叶斯、决策树、随机森林）的应用。 * **Scikit-learn实战：** 通过Scikit-learn库的案例，将理论知识转化为实际操作能力。 **课程收益：** 完成本课程后，您将能够： * 独立完成数据分析任务。 * 应用机器学习算法解决实际问题。 * 构建和评估有效的机器学习模型。 * 为开启数据科学或机器学习领域的职业生涯做好充分准备。 **适合人群：** * 希望进入数据科学和机器学习领域的初学者。 * 希望提升数据分析和机器学习技能的专业人士。 * 对Python编程和数据科学感兴趣的任何人。
This Course Cover Topics such as Python Basic Concepts, Python Advance Concepts, Numpy Library , Scipy Library , Pandas Library, Matplotlib Library, Seaborn Library, Plotlypy Library, Introduction to Data Science and steps to start Project in Data Science, Case Studies of Data Science and Machine Learning Algorithms such as Linear, Logistic, SVM, NLPThis is best course for any one who wants to start career in data science. with machine Learning.Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.The course provides path to start career in Data Analysis. Importance of Data, Collection of Data with Case Study is covered. Machine Learning Types such as Supervise Learning, Unsupervised Learning, are also covered. Machine Learning concept such as Train Test Split, Machine Learning Models, Model Evaluation are also covered. This Course will design to understand Machine Learning Algorithms with case Studies using Scikit Learn Library. The Machine Learning Algorithms such as Linear Regression, Logistic Regression, SVM, K Mean, KNN, Naïve Bayes, Decision Tree and Random Forest are covered with case studies
