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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-machine-learningtheoryprojectsa-z-90-hours/
课程评论：没有评论
课程名称：数据科学与机器学习（理论+项目）A-Z 90小时 课程概述：电气化无疑是20世纪最伟大的工程成就之一。从1821年电动机的发明到1827年的电路数学分析，尽管经历了数十年的发展，工厂、家庭和铁路的全面电气化才开始。今天，我们在人工智能（AI）领域也在经历类似的历程。尽管AI的正式成立是在1956年，但它最近才开始彻底改变人类的生活和工作方式。数据科学作为一个广阔且不断扩展的领域，涉及组织和提取数据洞察的系统和流程。机器学习（ML）是AI的重要分支，涉及开发能够自主学习和改进的系统。 本课程提供全面的数据科学与机器学习探索，深入覆盖这两个领域的基本概念。如今，组织生成了大量的数据，存储、分析和提取有意义的见解的能力是无价的。数据科学在此发挥着重要作用，着重于数据建模、仓储以及从原始数据中导出实用结果。数据科学家依赖AI与ML，这不仅有助于处理庞大的数据集，还提高了决策过程。 课程独特之处在于它提供理论基础和实用的实践经验，帮助您成为数据科学专业人士，准备将AI和ML概念应用于现实挑战。课程结构分为多个相互关联的部分，每一部分构建在前一部分的基础上，提供一个连贯的学习体验。 课程特点： - 全面而系统的进度，确保所有学习者（初学者和高级）都能跟上教材。 - 动手学习，包括实时编码、实践练习和真实项目。 - 接触最新的AI与ML进展，以及最前沿的模型和算法。 - 理论学习与实际应用的平衡，便于及时实践所学知识。 - 课程包含700多部高清教学视频、详细的代码笔记和评估任务，帮助您在每一部分学完后巩固知识。 课程内容概览： 1. **Python数据科学与分析**：解决问题的引入，学习复杂索引和数据可视化。 2. **Python的数据理解与可视化**：深入高级数据处理与可视化技巧，掌握Seaborn、Plotly等包。 3. **Python中的概率与统计**：掌握数据科学的理论基础，包括条件概率和统计推断。 4. **机器学习速成课程**：全面讲解机器学习的理论和实践。 5. **特征工程与降维**：了解数据准备在模型性能中的重要性。 6. **Python中的人工神经网络（ANN）**：学习TensorFlow深度学习框架，并应用ANN模型。 7. **Python中的卷积神经网络（CNN）**：深入理解CNN的应用，包括计算机视觉。 完成本课程后，您将能够理解数据科学与机器学习的关键原则和理论，使用真实数据集实施基于Python的机器学习模型，应用高级数据科学技术解决复杂问题，并自信地担任数据科学和机器学习的挑战角色。 适合人群： - 希望转型至数据科学的非工程背景人士。 - 渴望使用真实数据集的准数据科学家。 - 希望提升数据科学与机器学习专业知识的业务分析师。 - 对编程、数字和数据驱动决策充满热情的任何人。 现在就注册，开启您在数据科学与机器学习领域的精彩旅程，课程让复杂概念变得简单而有趣，学习过程充满收获。
Comprehensive Course Description:Electrification was undeniably one of the greatest engineering feats of the 20th century. The invention of the electric motor dates back to 1821, with mathematical analysis of electrical circuits following in 1827. However, it took several decades for the full electrification of factories, households, and railways to begin. Fast forward to today, and we are witnessing a similar trajectory with Artificial Intelligence (AI). Despite being formally founded in 1956, AI has only recently begun to revolutionize the way humanity lives and works.Similarly, Data Science is a vast and expanding field that encompasses data systems and processes aimed at organizing and deriving insights from data. One of the most important branches of AI, Machine Learning (ML), involves developing systems that can autonomously learn and improve from experience without human intervention. ML is at the forefront of AI, as it aims to endow machines with independent learning capabilities.Our "Data Science & Machine Learning Full Course in 90 Hours" offers an exhaustive exploration of both data science and machine learning, providing in-depth coverage of essential concepts in these fields. In today's world, organizations generate staggering amounts of data, and the ability to store, analyze, and derive meaningful insights from this data is invaluable. Data science plays a critical role here, focusing on data modeling, warehousing, and deriving practical outcomes from raw data.For data scientists, AI and ML are indispensable, as they not only help tackle large data sets but also enhance decision-making processes. The ability to transition between roles and apply these methodologies across different stages of a data science project makes them invaluable to any organization.What Makes This Course Unique?This course is designed to provide both theoretical foundations and practical, hands-on experience. By the end of the course, you will be equipped with the knowledge to excel as a data science professional, fully prepared to apply AI and ML concepts to real-world challenges.The course is structured into several interrelated sections, each of which builds upon the previous one. While you may initially view each section as an independent unit, they are carefully arranged to offer a cohesive and sequential learning experience. This allows you to master foundational skills and gradually tackle more complex topics as you progress.The "Data Science & Machine Learning Full Course in 90 HOURS" is crafted to equip you with the most in-demand skills in today's fast-paced world. The course focuses on helping you gain a deep understanding of the principles, tools, and techniques of data science and machine learning, with a particular emphasis on the Python programming language.Key Features:Comprehensive and methodical pacing that ensures all learners-beginners and advanced-can follow along and absorb the material.Hands-on learning with live coding, practical exercises, and real-world projects to solidify understanding.Exposure to the latest advancements in AI and ML, as well as the most cutting-edge models and algorithms.A balanced mix of theoretical learning and practical application, allowing you to immediately implement what you learn.The course includes over 700 HD video tutorials, detailed code notebooks, and assessment tasks that challenge you to apply your knowledge after every section. Our instructors, passionate about teaching, are available to provide support and clarify any doubts you may have along your learning journey.Course Content Overview:Python for Data Science and Data Analysis:Introduction to problem-solving, leading up to complex indexing and data visualization with Matplotlib.No prior knowledge of programming is required.Master data science packages such as NumPy, Pandas, and Matplotlib.After completing this section, you will have the skills necessary to work with Python and data science packages, providing a solid foundation for transitioning to other programming languages.Data Understanding and Visualization with Python:Delve into advanced data manipulation and visualization techniques.Explore widely used packages, including Seaborn, Plotly, and Folium, for creating 2D/3D visualizations and interactive maps.Gain the ability to handle complex datasets, reducing your dependency on core Python language and enhancing your proficiency with data science tools.Mastering Probability and Statistics in Python:Learn the theoretical foundation of data science by mastering Probability and Statistics.Understand critical concepts like conditional probability, statistical inference, and estimations-key pillars for ML techniques.Explore practical applications and derive important relationships through Python code.Machine Learning Crash Course:A thorough walkthrough of the theoretical and practical aspects of machine learning.Build machine learning pipelines using Sklearn.Dive into more advanced ML concepts and applications, preparing you for deeper exploration in subsequent sections.Feature Engineering and Dimensionality Reduction:Understand the importance of data preparation for improving model performance.Learn techniques for selecting and transforming features, handling missing data, and enhancing model accuracy and efficiency.The section includes real-world case studies and coding examples in Python.Artificial Neural Networks (ANNs) with Python:ANNs have revolutionized machine learning with their ability to process large amounts of data and identify intricate patterns.Learn the workings of TensorFlow, Google's deep learning framework, and apply ANN models to real-world problems.Convolutional Neural Networks (CNNs) with Python:Gain a deep understanding of CNNs, which have revolutionized computer vision and many other fields, including audio processing and reinforcement learning.Build and train CNNs using TensorFlow for various applications, from facial recognition to neural style transfer.By the End of This Course, You Will Be Able To:Understand key principles and theories in Data Science and Machine Learning.Implement Python-based machine learning models using real-world datasets.Apply advanced data science techniques to solve complex problems.Take on challenging roles in data science and machine learning with confidence.Who Should Enroll:Individuals from non-engineering backgrounds eager to transition into Data Science.Aspiring data scientists who want to work with real-world datasets.Business analysts looking to gain expertise in Data Science & ML.Anyone passionate about programming, numbers, and data-driven decision-making.Enroll now and start your exciting journey in the fields of Data Science and Machine Learning. This course simplifies even the most complex concepts and makes learning a rewarding experience.
