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人工智能-必备数学基础

唐宇迪唐

4.8

Based on 238 ratings

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唐宇迪唐

数据科学与人工智能必备数学基础课程旨在帮助同学们快速打下数学基础，通俗讲解其中每一个知识点。课程内容涉及高等数学，线性代数，概率论与统计学，同学们在学习过程中应当以理解为出发点并不需要死记每一个公式，快速掌握核心知识点。课程章节内容较多，零基础同学按顺序学习即可，有基础的同学们可以按照自己的需求来有选择的学习！

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What's inside

Syllabus

高等数学基础

课程简介

函数

极限

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Covers core topics and offers a strong introduction to this subject

Provides lessons on foundational high-level math concepts

Useful for understanding basic high-level math

This course's material may be suitable for beginners

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Reviews summary

Ai数学基础：清晰入门与全面覆盖

根据学员反馈，这门“人工智能-必备数学基础”课程因其数学概念解释清晰易懂而受到广泛好评，尤其适合零基础或需要系统复习的AI及数据科学学习者。课程内容全面，涵盖了高等数学、线性代数、概率论与统计学，为后续的AI学习打下了坚实的基础。许多学生认为理论与实际案例结合得很好，例如汽车价格预测。不过，部分有数学基础的学员提到课程深度可能不足以应对高级AI算法，且有评论建议增加Python代码实现与习题实践。早期评论中提及的视频质量问题在近期评论中已较少出现，显示课程可能有所改进。

早期评论提及视频音质或画面问题，近期有所改善。

"课程很好，但视频质量有些参差不齐，偶尔有声音小或者画面模糊的情况。"

"我注意到早期评论提到的一些技术问题，但在我学习时，视频和音频质量都很好，没有任何干扰。"

将抽象数学理论与实际应用案例相结合。

"...案例分析也很实用，比如汽车价格预测，让我能将理论与实践结合。"

"案例分析很有启发性，对我理解极大似然估计很有帮助。感觉学完后对AI的数学原理有了宏观的认识。"

"我认为从概念到应用都有涉及，这门课是我进入数据科学领域的不可或缺的垫脚石。"

课程兼顾零基础和有一定基础的学习者。

"对于我这种零基础的AI学习者来说真是太友好了。"

"有基础的同学们可以按照自己的需求来有选择的学习！"

"课程既照顾了初学者，也为有一定基础的同学提供了复习和深化理解的机会，选择性学习模式很好。"

覆盖AI所需数学基础，构建完整知识体系。

"课程内容覆盖面广，知识点非常系统。老师的讲解大部分都很棒..."

"非常好的一门入门课程，数学知识点很全面，讲解逻辑性强，从概念到应用都有涉及。"

"我觉得对于想进入数据科学领域的同学，这门课是不可或缺的垫脚石。"

深入浅出地讲解复杂数学概念，适合初学者。

"这门课程的数学概念解释非常清晰，老师能够用通俗易懂的方式讲解高等数学、线性代数和概率论..."

"我之前对数学很头疼，但这门课让我觉得数学不再那么枯燥了，老师深入浅出，特别是泰勒公式和SVD的讲解，让我茅塞顿开。"

"老师讲得真好，把枯燥的数学讲得生动有趣。从微积分到线性代数再到概率论，都讲到了。"

学员期望增加更多习题或代码实现部分。

"习题和课后实践如果能再多一些就好了，这样能更好地巩固知识。"

"唯一的建议是希望有更多Python代码实现的部分，方便直接应用。"

"我希望能有更多练习题来巩固我所学的知识，或者有更多的实际编程示例。"

对于进阶学习者，课程深度可能不足。

"作为一名有一定数学基础的学员，感觉课程对于基础概念的讲解过多，内容深度不够，对于进阶AI算法所需的数学支撑不够。"

"更适合完全初学者。希望未来能有更多高阶主题。"

"一些有基础的同学可能会觉得内容偏基础，更希望能够涉及更深入的数学理论或进阶算法背后的数学原理。"

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Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in 人工智能-必备数学基础 with these activities:

探索 Python 库

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探索 Python 库可以帮助学生了解用于数据科学和人工智能的工具，为后续课程内容做好准备。

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选择一个与课程相关的 Python 库，如 NumPy、Pandas 或 Scikit-learn。
查找在线教程或文档来学习库的基本功能。
练习使用库进行简单的数据操作或建模任务。

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Career center

Learners who complete 人工智能-必备数学基础 will develop knowledge and skills that may be useful to these careers:

Statistician

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Machine Learning Engineer

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Data Analyst

Data Analysts clean, analyze, and visualize data to identify trends and patterns, providing insights to inform decision-making. This course can be helpful as it covers fundamental mathematical concepts such as probability, statistics, and regression analysis. These concepts are essential for understanding data, performing statistical analysis, and presenting data-driven insights.

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Data Scientist

Data Scientists create mathematical models to identify trends and patterns in data to advance scientific research and innovation. This course may be useful for aspiring Data Scientists as it covers essential mathematical principles, including statistics, probability, and linear algebra, which serve as foundational building blocks for data science and machine learning.

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Operations Research Analyst

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Quantitative Analyst

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Research Analyst

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Software Engineer

Software Engineers design, develop, and maintain software applications. This course may be helpful for aspiring Software Engineers who wish to specialize in data-driven software development or machine learning applications. The coverage of mathematical concepts such as probability, statistics, and linear algebra can provide a solid foundation for understanding data structures, algorithms, and modeling techniques used in software engineering.

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Economist

Economists analyze economic data and trends to understand economic behavior and develop policies. This course may be useful for aspiring Economists as it covers essential mathematical concepts such as probability, statistics, and regression analysis. These concepts are used in economic modeling, forecasting, and data analysis to understand economic phenomena and make informed policy decisions.

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Financial Risk Manager

Financial Risk Managers assess and manage financial risks within organizations. This course may be valuable for aspiring Financial Risk Managers as it provides a strong foundation in probability, statistics, and regression analysis. These concepts are essential for understanding and managing financial risks, developing risk models, and making informed decisions in financial risk management.

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Actuary

Actuaries analyze the financial implications of risk and uncertainty, advising companies on financial planning. This course can be particularly helpful as it provides a strong foundation in probability and statistics, which are essential concepts in actuarial work. Furthermore, the course's coverage of regression analysis may be beneficial for understanding insurance pricing models.

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Biostatistician

Biostatisticians use statistical methods to analyze and interpret health-related data, contributing to medical research and public health. This course may be useful for aspiring Biostatisticians as it covers fundamental concepts of probability, statistics, and regression analysis. Understanding these topics is crucial for designing and conducting biomedical studies and interpreting the results.

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Financial Analyst

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Business Analyst

Business Analysts use data analysis and statistical techniques to help organizations make informed business decisions. This course can be useful as it covers essential mathematical foundations such as probability, statistics, and regression analysis. These concepts are crucial for understanding data, identifying trends, and making data-driven recommendations in a business context.

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Risk Manager

Risk Managers identify, assess, and mitigate risks within organizations. This course may be helpful as it covers fundamental concepts of probability and statistics, which are essential for understanding and managing risk. Additionally, the course's coverage of regression analysis can be beneficial for analyzing risk data and developing risk models.

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Reading list

We've selected 15 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in 人工智能-必备数学基础.