PYS

使用Python教学大纲的机器学习

数据科学和人工智能依靠机器学习为基础，而Python是首选的编程语言。本课程提供了使用Python进行机器学习的动手培训，涵盖了受监督和无监督的学习，统计建模以及各种算法和模型的基础。学生将探索机器学习及其对社会的影响的现实应用。课程亮点包括： *监督与无监督的学习：了解这两种方法之间的差异 *统计建模和机器学习：它们如何相互关系：流行算法 *流行算法：分类，回归，聚类，尺寸还原，尺寸还原 *流行模型：火车/测试分裂，root平均误差，root平均正方形，随机森林，五个模型 监督与无监督的学习2。 有监督的学习（IK-Nearest邻居，决策树，随机森林）3。 监督学习II（回归算法，模型评估）4。 无监督的学习（K-均值聚类，分层聚类，基于密度的聚类）5。 Dimensionality Reduction & Collaborative Filtering Recommended skills prior to taking this course include: * Hands-on experience with Jupyter tool * Working knowledge of Python programming language as it applies to data analytics * Familiarity with Data Analysis with Python The course staff includes experts in the field of Machine Learning and Data Science, including Saeed Aghabozorgi, PhD, a Sr. Data Scientist at IBM, and Kevin Wong, Technical课程开发人员，已经开发了有关大数据和机器学习的课程。监督与无监督的学习2。有监督的学习（IK-Nearest邻居，决策树，随机森林）3。监督学习II（回归算法，模型评估）4。无监督的学习（K-均值聚类，分层聚类，基于密度的聚类）5。Dimensionality Reduction & Collaborative Filtering Recommended skills prior to taking this course include: * Hands-on experience with Jupyter tool * Working knowledge of Python programming language as it applies to data analytics * Familiarity with Data Analysis with Python The course staff includes experts in the field of Machine Learning and Data Science, including Saeed Aghabozorgi, PhD, a Sr. Data Scientist at IBM, and Kevin Wong, Technical课程开发人员，已经开发了有关大数据和机器学习的课程。Machine Learning Course Overview ------------------------------- This course provides an introduction to machine learning using the Python programming language, covering supervised and unsupervised learning, deep learning, image processing, and generative adversarial networks.该课程是为没有事先编程或计算机科学背景的个人而设计的，专注于实际应用和技术，而不是统计方法。Key Topics ------------ * Machine learning fundamentals * Supervised and unsupervised learning techniques * Deep learning concepts * Image processing and generative adversarial networks * Scikit-learn toolkit introduction * Clustering and dimensionality reduction * Model evaluation, tuning, and practical projects in Jupyter Notebooks Course Requirements ------------------- The course is open to anyone interested in learning Python programming and machine learning.建议在Python中介绍数据科学并在Python中进行绘图，图表和数据表示。目标------------ *学习受监督和无监督技术之间的差异 *确定适合特定数据集的技术和需求 *工程师的功能 *以满足特定需求 *编写Python代码以进行分析 *获得分析的实践经验 *通过Scikit-Leartor和其他图书馆在此课程中，与他们最终的py newers a Outs Authers Pys Outs Angine conguts of Scikit-Leargie nocal of Machine conform n of Machine conform n of Machige a Grachips Py Accorment Py newers nocal of Py noce py，该领域的从业者。立即开始您的机器学习冒险，并通过获取雇主大声疾呼的技能来增强您的简历。今天就注册以在就业市场上获得竞争优势！