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所在平台: Udemy |
课程主页: https://www.udemy.com/course/complete-data-science-bootcamp/
课程评论：没有评论
课程名称：完整数据科学技能培训营 概述：数据科学是一门涉及多种技术和方法的领域，用于从数据中提取洞察和知识。机器学习（ML）和深度学习（DL）是数据科学的两个子集，它们通常结合使用来分析和理解数据。数据科学中，ML算法常用于构建基于历史数据的预测模型，这些模型可以用于分类、回归和聚类等任务。常见的ML算法包括线性回归、决策树和k均值算法。另一方面，DL是基于具有多个层次的人工神经网络的ML子集，通过经验学习和改进，特别适用于图像识别、语音识别和自然语言处理等任务。DL算法包括卷积神经网络（CNN）和递归神经网络（RNN）。 在数据科学项目中，DL模型通常与特征工程、数据清理和可视化等其他技术结合使用，以从数据中提取洞察和知识。例如，DL模型可以自动从图像中提取特征，这些特征随后可以用于传统的ML模型。 总结来说，数据科学是一个涵盖各种技术和方法的领域，旨在从数据中提取洞察和知识。ML和DL是数据科学的子集，分别用于构建预测模型和建模复杂的模式与关系。ML和DL常在数据科学项目中结合使用，以提取数据中的洞察和知识。 在本课程中你将学习到： - 数据科学项目的生命周期 - Python库如Pandas和Numpy的广泛应用 - 使用Matplotlib和Seaborn进行数据可视化 - 数据预处理步骤，如特征编码、特征缩放等 - 机器学习基础知识及不同算法 - 云计算在机器学习中的应用 - 深度学习相关的5个项目，如糖尿病预测、股票价格预测等 祝你学习顺利！
Data science is the field that encompasses the various techniques and methods used to extract insights and knowledge from data. Machine learning (ML) and deep learning (DL) are both subsets of data science, and they are often used together to analyze and understand data.In data science, ML algorithms are often used to build predictive models that can make predictions based on historical data. These models can be used for tasks such as classification, regression, and clustering. ML algorithms include linear regression, decision trees, and k-means.DL, on the other hand, is a subset of ML that is based on artificial neural networks with multiple layers, which allows the system to learn and improve through experience. DL is particularly well-suited for tasks such as image recognition, speech recognition, and natural language processing. DL algorithms include convolutional neural networks (CNNs) and recurrent neural networks (RNNs).In a data science project, DL models are often used in combination with other techniques such as feature engineering, data cleaning, and visualization, to extract insights and knowledge from data. For instance, DL models can be used to automatically extract features from images, and then these features can be used in a traditional ML model.In summary, Data science is the field that encompasses various techniques and methods to extract insights and knowledge from data, ML and DL are subsets of data science that are used to analyze and understand data, ML is used to build predictive models and DL is used to model complex patterns and relationships in data. Both ML and DL are often used together in data science projects to extract insights and knowledge from data.IN THIS COURSE YOU WILL LEARN ABOUT:Life Cycle of a Data Science Project.Python libraries like Pandas and Numpy used extensively in Data Science.Matplotlib and Seaborn for Data Visualization.Data Preprocessing steps like Feature Encoding, Feature Scaling etc...Machine Learning Fundamentals and different algorithmsCloud Computing for Machine LearningDeep Learning 5 projects like Diabetes Prediction, Stock Price Prediction etc...ALL THE BEST!!!
