应用机器学习课程
应用机器学习课程可以帮助您学习数据预处理、模型选择、Feature Engineering 和 Evaluation 指标。您可以掌握在实际环境中实施算法、优化性能和解释结果的技能。许多课程介绍 Python、TensorFlow 和 Scikit-learn 等工具，这些工具支持开发机器学习模型和应用 AI 技术解决实际问题。
热门应用机器学习课程和认证
状态：免费试用University of Michigan
您将获得的技能: Python 程序设计, 机器学习, 分类算法, 无监督学习, 预测建模, Scikit-learn （机器学习库）, 人工神经网络, 模型评估, 监督学习, 功能工程, 回归分析, 决策树学习, 应用机器学习, 随机森林算法
状态：免费试用Johns Hopkins University
您将获得的技能: Computer Vision, Model Evaluation, PyTorch (Machine Learning Library), Supervised Learning, Unsupervised Learning, Image Analysis, Applied Machine Learning, Data Preprocessing, Dimensionality Reduction, Reinforcement Learning, Feature Engineering, Machine Learning Algorithms, Convolutional Neural Networks, Regression Analysis, Data Processing, Machine Learning, Data Mining, Data Cleansing, Deep Learning, Artificial Neural Networks
状态：新The Cleveland Clinic Foundation
您将获得的技能: Machine Learning Methods, Machine Learning, Machine Learning Algorithms, Healthcare Industry Knowledge, Health Informatics, Applied Machine Learning, Clinical Informatics, Health Care, Supervised Learning, Clinical Experience, Emerging Technologies, Artificial Intelligence, Reinforcement Learning, Data-Driven Decision-Making, Data Science
状态：新状态：免费试用Dartmouth College
您将获得的技能: Supervised Learning, Bayesian Network, Logistic Regression, Artificial Neural Networks, Machine Learning Methods, Statistical Modeling, Predictive Modeling, Model Evaluation, Statistical Machine Learning, Probability & Statistics, Bayesian Statistics, Deep Learning, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning, Machine Learning Algorithms, Statistical Methods, Artificial Intelligence, Regression Analysis, Classification Algorithms, Statistical Inference
状态：免费试用多位教师
您将获得的技能: 机器学习, 分类算法, 预测建模, Scikit-learn （机器学习库）, 监督学习, 深度学习, 模型评估, 迁移学习, 应用机器学习, Jupyter, 决策树学习, 无监督学习, 人工智能, 强化学习, 数据预处理, 功能工程, NumPy, 数据伦理, 张力流, 随机森林算法
状态：免费试用Alberta Machine Intelligence Institute
您将获得的技能: 业务要求, 机器学习, 监督学习, 案例研究, 应用机器学习, 数据伦理, 无监督学习, 机器学习算法, 人工智能, 产品生命周期管理, 数据收集, 数据预处理, 数据质量, 商业分析
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: Python 程序设计, 逻辑回归, 机器学习, 分类算法, 预测建模, Scikit-learn （机器学习库）, 模型评估, 监督学习, 回归分析, 数据预处理, 无监督学习, 人工智能, Jupyter, 功能工程, NumPy
状态：免费试用University of Washington
您将获得的技能: 计算机视觉, 逻辑回归, 统计建模, 机器学习, 分类算法, 预测建模, 贝叶斯统计, 无监督学习, 模型评估, 监督学习, 深度学习, 数据预处理, 统计机器学习, 决策树学习, 人工智能, 功能工程, 回归分析, 应用机器学习, 数据挖掘, 图像分析
状态：新状态：免费试用您将获得的技能: Model Evaluation, Classification Algorithms, Regression Analysis, Matplotlib, Feature Engineering, Time Series Analysis and Forecasting, Data Preprocessing, Jupyter, Image Analysis, Cloud Deployment, Scikit Learn (Machine Learning Library), Applied Machine Learning, Tensorflow, Amazon Web Services, Python Programming, Data Transformation, Logistic Regression, Machine Learning Methods, Machine Learning, Artificial Intelligence and Machine Learning (AI/ML)
状态：免费试用New York University
您将获得的技能: 机器学习, 金融交易, 深度学习, Scikit-learn （机器学习库）, 预测建模, 监督学习, 模型评估, 马尔可夫模型, 人工神经网络, 应用机器学习, 统计方法, 强化学习, 回归分析, 无监督学习, 张力流, 市场流动性, 衍生产品, 决策树学习, 金融市场, 降维
状态：预览Duke University
您将获得的技能: PyTorch（机器学习库）, Python 程序设计, 计算机视觉, 卷积神经网络, 深度学习, 逻辑回归, 机器学习, 自然语言处理, 人工神经网络, 无监督学习, 迁移学习, 监督学习, 医学影像, 强化学习, 应用机器学习, 图像分析, 递归神经网络 (RNN)
状态：新状态：免费试用您将获得的技能: Healthcare Ethics, Health Informatics, Machine Learning, Deep Learning, Machine Learning Software, Clinical Informatics, Statistical Machine Learning, Convolutional Neural Networks, Data Preprocessing, Predictive Modeling, Electronic Medical Record System, Machine Learning Algorithms, Recurrent Neural Networks (RNNs), Model Evaluation, Data Analysis
总之，以下是 10 最受欢迎的 applied machine learning 课程
- Python 中的应用机器学习: University of Michigan
- Applied Machine Learning: Johns Hopkins University
- Machine Learning in Healthcare: Foundations and Applications: The Cleveland Clinic Foundation
- Practical Machine Learning: Foundations to Neural Networks: Dartmouth College
- 机器学习: DeepLearning.AI
- 应用机器学习入门: Alberta Machine Intelligence Institute
- 监督式机器学习：回归与分类: DeepLearning.AI
- 机器学习: University of Washington
- AI Driven Machine Learning with Python: EDUCBA
- 金融领域的机器学习和强化学习: New York University
