机器学习数学课程
Machine Learning 数学课程通常教授线性代数、微积分、概率和统计学，为理解算法打下坚实的基础。您可以掌握优化技术、数据分析和模型评估方面的技能，这些技能对于有效的机器学习应用至关重要。许多课程都会介绍 NumPy 和 TensorFlow 等 Python 库等工具，帮助您在 AI 项目中实现数学概念，提高您使用数据驱动模型的能力。
机器学习热门数学课程和认证
状态：免费试用Alberta Machine Intelligence Institute
您将获得的技能: 模型培训, Data Validation, 数据质量, 计算机编程, 统计分析, 数据清理, 功能工程, 验证和确认, 数据验证, 算法, 机器学习, Algorithm, 线性代数, Model Evaluation, 机器学习算法, 数据转换, Python 编程, 负责任的人工智能, 数据预处理, 模型评估, 应用机器学习
状态：免费试用DeepLearning.AI
您将获得的技能: 模型优化, 降维, 描述性统计, 微积分, 统计推理, 应用数学, 概率与统计, 概率分布, 统计资料, 机器学习, 线性代数, 数据转换, 数学软件, 机器学习方法, 统计假设检验, 抽样（统计）, 贝叶斯统计, 统计方法, 应用机器学习, 概率
状态：免费试用Imperial College London
您将获得的技能: 数据科学, 模型优化, NumPy, 降维, 描述性统计, 数据操作, 回归分析, 微积分, 统计资料, 无监督学习, 应用数学, 线性代数, 数据转换, 几何学, 数学软件, Jupyter, 人工神经网络, 数值分析, 统计方法, 应用机器学习
状态：新状态：免费试用您将获得的技能: Feature Engineering, Model Evaluation, Model Deployment, Fine-tuning, Data Preprocessing, Model Training, Deep Learning, Machine Learning Methods, Model Optimization, Scikit Learn (Machine Learning Library), PyTorch (Machine Learning Library), Scalability, Hugging Face, Docker (Software), Supervised Learning, Machine Learning Algorithms, Applied Machine Learning, Application Deployment, Software Development, Machine Learning
状态：免费试用DeepLearning.AI
您将获得的技能: 降维, 应用数学, 代数, 数据操作, 图像分析, 机器学习, 线性代数, 数据转换, 机器学习方法, 数学软件, Python 编程
状态：免费试用Imperial College London
您将获得的技能: 数据科学, 应用数学, 数据操作, 机器学习, 线性代数, 数据转换, Jupyter
是什么让您今天来到 Coursera？
状态：免费试用DeepLearning.AI
您将获得的技能: 数据科学, 统计分析, 柱状图, 统计推理, 描述性统计, 探索性数据分析, 概率分布, 概率与统计, 箱形图, 统计资料, A/B 测试, 统计假设检验, 抽样（统计）, 贝叶斯统计, 统计可视化, 统计机器学习, 统计方法, 相关性分析, 概率
状态：免费试用University of Pittsburgh
您将获得的技能: Statistical Analysis, NumPy, Probability Distribution, Matplotlib, Plot (Graphics), Statistics, Data Science, Probability & Statistics, Probability, Statistical Modeling, Predictive Modeling, Data Analysis, Linear Algebra, Predictive Analytics, Statistical Methods, Mathematics and Mathematical Modeling, Applied Mathematics, Python Programming, Machine Learning, Logical Reasoning
状态：免费试用状态：人工智能技能University of Pennsylvania
您将获得的技能: Statistical Machine Learning, Data Preprocessing, Model Evaluation, PyTorch (Machine Learning Library), Statistical Methods, Probability, Probability & Statistics, Sampling (Statistics), Logistic Regression, Deep Learning, Probability Distribution, Statistical Modeling, Python Programming, Supervised Learning, Machine Learning, Data Processing, Agentic systems, Artificial Intelligence, Algorithms, AI literacy
状态：免费试用DeepLearning.AI
您将获得的技能: 深度学习, 模型优化, 计算机编程, 应用数学, 微积分, 机器学习, 数据可视化, 机器学习方法, Python 编程, 数学建模, 衍生产品, 人工神经网络, 数值分析, 应用机器学习
状态：预览Johns Hopkins University
您将获得的技能: Descriptive Statistics, Linear Algebra, Exploratory Data Analysis, Data-Driven Decision-Making, Data Analysis, Bayesian Statistics, Artificial Intelligence, Probability, Regression Analysis, Calculus, Mathematical Software, Advanced Mathematics, Applied Mathematics, Probability Distribution, Mathematical Modeling, Model Optimization, Integral Calculus, Algebra, Machine Learning Algorithms, Dimensionality Reduction
状态：免费试用您将获得的技能: Feature Engineering, Decision Tree Learning, Applied Machine Learning, Supervised Learning, Advanced Analytics, Statistical Machine Learning, Machine Learning, Machine Learning Algorithms, Unsupervised Learning, Analytics, Model Training, Random Forest Algorithm, Model Optimization, Predictive Modeling, Model Evaluation, Python Programming, Performance Tuning, Classification Algorithms
总之，以下是 10 最受欢迎的 math for machine learning 课程
- 机器学习数据: Alberta Machine Intelligence Institute
- 机器学习和数据科学数学: DeepLearning.AI
- Mathematics for Machine Learning: Imperial College London
- Machine Learning and Deep Learning for Software Engineers: Board Infinity
- 机器学习和数据科学线性代数: DeepLearning.AI
- Mathematics for Machine Learning: Linear Algebra: Imperial College London
- 机器学习和数据科学的概率与统计: DeepLearning.AI
- Mathematical Foundations for Data Science and Analytics: University of Pittsburgh
- AI and Machine Learning Essentials with Python: University of Pennsylvania
- 机器学习和数据科学微积分: DeepLearning.AI
