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所在平台: Udemy |
课程主页: https://www.udemy.com/course/understanding-algorithmic-foundations-of-ai-ml/
课程评论：没有评论
课程名称：理解人工智能与机器学习的算法基础 课程概述：本课程旨在揭示支撑人工智能（AI）和机器学习（ML）系统的复杂算法。通过深入探讨推动该领域的基本数学和计算原理，使学员能够理解这些强大技术的内部工作原理。 大目标： - 打下坚实基础：全面理解AI和ML背后的核心算法原理。 - 实用技能：学习设计和实现基本算法，利用AI和ML技术解决现实问题。 主要主题： - 监督学习与无监督学习：探讨不同的方法在有标签和无标签数据的情况下训练机器学习模型。 - 回归与分类：理解如何预测连续值（回归）与将数据分类（分类）。 - 决策树与随机森林：搞清楚这些用于决策和预测的强大算法。 - 神经网络：深入研究神经网络的结构和原理，这是深度学习的基础。 - 实验算法：发现实验和设计新算法的方法。 - 评估与优化：学习评估算法性能和优化算法以提升结果的基本策略。 通过本课程的学习，学员将获得以下知识和技能： - 理解推动AI和ML系统的关键算法。 - 应用算法解决现实问题。 - 评估不同算法在各种环境中的有效性。
Unravel the intricate algorithms that underpin Artificial Intelligence (AI) and Machine Learning (ML) systems. This course delves into the fundamental mathematical and computational principles driving the field, empowering you to grasp the inner workings of these powerful technologies.Big Picture Goals:Gain a solid foundation: Develop a comprehensive understanding of the core algorithmic principles behind AI and ML.Practical Skills: Learn to design and implement basic algorithms to tackle real-world problems using AI and ML techniques.Key Topics:Supervised and Unsupervised Learning: Explore the distinct approaches used to train machine learning models, with and without labeled data.Regression and Classification: Understand how to predict continuous values (regression) and classify data into categories (classification).Decision Trees and Random Forests: Demystify these powerful algorithms for decision-making and prediction.Neural Networks: Dive into the architecture and principles of neural networks, the backbone of deep learning.Experimental Algorithms: Discover methods for experimenting with and designing new algorithms.Evaluation and Optimization: Learn essential strategies to evaluate algorithm performance and optimize them for enhanced results.By the end of this course, you will have the knowledge and skills to:Understand the key algorithms that power AI and ML systems.Apply algorithms to solve real-world problems.Evaluate the effectiveness of different algorithms in various contexts.
