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
University of Michigan
您将获得的技能: 分类算法, 无监督学习, 预测建模, 人工神经网络, 监督学习, 机器学习, 随机森林算法, Model Evaluation, Python 程序设计, 决策树学习, 功能工程, 应用机器学习, Scikit-learn （机器学习库）, 回归分析
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
您将获得的技能: Supervised Learning, Feature Engineering, Applied Machine Learning, Unsupervised Learning, Model Evaluation, Machine Learning Algorithms, Predictive Modeling, Data Preprocessing, Classification Algorithms, Artificial Intelligence and Machine Learning (AI/ML), Dimensionality Reduction, Performance Tuning
您将获得的技能: Model Evaluation, Classification Algorithms, Regression Analysis, Data Science, Statistical Modeling, Predictive Modeling, Exploratory Data Analysis, Machine Learning, Data Analysis, Applied Machine Learning, Machine Learning Algorithms, Feature Engineering, Random Forest Algorithm, Supervised Learning, Logistic Regression, Data Processing, Data Preprocessing, Predictive Analytics, Data Manipulation, Data Visualization
状态：AI 趋势DeepLearning.AI
您将获得的技能: 人工智能, 分类算法, 预测建模, 无监督学习, NumPy, 监督学习, 逻辑回归, Python 程序设计, 机器学习, 数据预处理, Jupyter, Scikit-learn （机器学习库）, Model Evaluation, 回归分析, 功能工程
状态：AI 趋势多位教师
您将获得的技能: 人工智能, 张力流, 分类算法, Transfer Learning, 无监督学习, 预测建模, NumPy, 监督学习, 深度学习, 数据伦理, Jupyter, 机器学习, 应用机器学习, 随机森林算法, 强化学习, Model Evaluation, 数据预处理, Scikit-learn （机器学习库）, 功能工程, 决策树学习
是什么让您今天来到 Coursera？
Alberta Machine Intelligence Institute
您将获得的技能: 人工智能, 业务要求, 产品生命周期管理, 监督学习, 无监督学习, 数据质量, 机器学习, 机器学习算法, 数据伦理, 数据收集, 应用机器学习, 案例研究, 数据预处理, 商业分析
状态：AI 趋势您将获得的技能: Generative AI, Model Evaluation, Supervised Learning, Generative Model Architectures, Recurrent Neural Networks (RNNs), Unsupervised Learning, Data Preprocessing, Large Language Modeling, Time Series Analysis and Forecasting, Exploratory Data Analysis, LLM Application, Applied Machine Learning, Generative Adversarial Networks (GANs), Retrieval-Augmented Generation, Data Collection, Machine Learning Algorithms, Convolutional Neural Networks, Model Deployment, Transfer Learning, Hugging Face
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：AI 趋势University of Washington
您将获得的技能: 人工智能, 预测建模, 分类算法, 无监督学习, 贝叶斯统计, 监督学习, 计算机视觉, 统计机器学习, 深度学习, 机器学习, 逻辑回归, 数据预处理, 功能工程, 统计建模, 决策树学习, 应用机器学习, 图像分析, Model Evaluation, 回归分析, 数据挖掘
New York University
您将获得的技能: 张力流, 预测建模, 无监督学习, 监督学习, 深度学习, 降维, 衍生产品, 人工神经网络, 金融交易, 马尔可夫模型, 统计方法, 机器学习, 应用机器学习, 市场流动性, Scikit-learn （机器学习库）, 金融市场, 回归分析, 强化学习, Model Evaluation, 决策树学习
University of Washington
您将获得的技能: 人工智能, 无监督学习, 自然语言处理, 分类算法, 监督学习, 预测建模, 计算机视觉, 深度学习, Model Evaluation, 机器学习, Python 程序设计, 数据预处理, 功能工程, 文本挖掘, 应用机器学习, 图像分析, 回归分析, 应用程序开发
是什么让您今天来到 Coursera？
与 applied machine learning 相关的搜索
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
- Applied Machine Learning and Model Optimization: Packt
- Applied Machine Learning Without Coding: Edureka
- 监督式机器学习：回归与分类: DeepLearning.AI
- 机器学习: DeepLearning.AI
- 应用机器学习入门: Alberta Machine Intelligence Institute
- Machine Learning with Scikit-learn, PyTorch & Hugging Face: Coursera
- AI Driven Machine Learning with Python: EDUCBA
- 机器学习: University of Washington
