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: 监督学习, 预测建模, Python 编程, 功能工程, 人工神经网络, 分类算法, 模型优化, 无监督学习, Model Evaluation, 机器学习, Scikit Learn（机器学习库）, 模型评估, 机器学习算法, 机器学习方法, 应用机器学习, 模型培训
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, Machine Learning Methods, Reinforcement Learning, Feature Engineering, Machine Learning Algorithms, Convolutional Neural Networks, Regression Analysis, Data Processing, Model Training, Machine Learning, Deep Learning, Model Optimization
多位教师
您将获得的技能: 预测建模, 迁移学习, Transfer Learning, 负责任的人工智能, 监督学习, Jupyter, 决策树学习, 深度学习, 分类算法, Model Evaluation, 人工智能, 机器学习, 无监督学习, Scikit Learn（机器学习库）, NumPy, 机器学习算法, 数据伦理, 模型评估, 应用机器学习, 模型培训, 机器学习方法, 张力流
您将获得的技能: Model Evaluation, Classification Algorithms, Regression Analysis, Data Science, Statistical Modeling, Predictive Modeling, Machine Learning Methods, Exploratory Data Analysis, Machine Learning, Data Analysis, Applied Machine Learning, Machine Learning Software, Feature Engineering, Random Forest Algorithm, Supervised Learning, Logistic Regression, Data Processing, Model Optimization, Data Manipulation, Data Visualization
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, Data Collection, Model Optimization, Convolutional Neural Networks, Model Deployment, Transfer Learning, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning Methods, Machine Learning Software
University of Washington
您将获得的技能: 图像分析, 监督学习, 预测建模, 数据挖掘, 人工智能和机器学习（AI/ML）, 功能工程, 分类算法, 统计机器学习, 逻辑回归, 无监督学习, Model Evaluation, 机器学习, 回归分析, 统计建模, 贝叶斯统计, 机器学习算法, 人工智能个性化, 模型评估, 机器学习方法, 模型培训, 应用机器学习
是什么让您今天来到 Coursera？
Alberta Machine Intelligence Institute
您将获得的技能: 案例研究, 数据收集, 监督学习, 数据采集, 业务分析, 无监督学习, 人工智能, 机器学习, 数据质量, 产品生命周期管理, 数据伦理, 机器学习算法, 业务要求, 应用机器学习, 机器学习方法, 数据预处理
Google Cloud
您将获得的技能: Model Deployment, Model Optimization, Convolutional Neural Networks, Google Cloud Platform, Natural Language Processing, Tensorflow, MLOps (Machine Learning Operations), Large Language Modeling, Reinforcement Learning, Model Training, Transfer Learning, Computer Vision, Keras (Neural Network Library), Systems Design, Applied Machine Learning, Image Analysis, AI Personalization, Cloud Deployment, Recurrent Neural Networks (RNNs), Machine Learning
您将获得的技能: Supervised Learning, Model Optimization, Feature Engineering, Applied Machine Learning, Unsupervised Learning, Model Evaluation, Machine Learning Methods, Statistical Machine Learning, Machine Learning Algorithms, Predictive Modeling, Model Training, Data Preprocessing, Classification Algorithms, Artificial Intelligence and Machine Learning (AI/ML), Dimensionality Reduction, Data Transformation, Fine-tuning
New York University
您将获得的技能: 金融交易, 金融市场, 预测建模, 监督学习, 降维, 人工神经网络, 统计方法, 衍生产品, 决策树学习, 统计机器学习, 无监督学习, Model Evaluation, 机器学习, 强化学习, 统计建模, 机器学习算法, 机器学习方法, 模型评估, 张力流, 应用机器学习, 模型培训
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)
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
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
- 机器学习: DeepLearning.AI
- Applied Machine Learning Without Coding: Edureka
- Machine Learning with Scikit-learn, PyTorch & Hugging Face: Coursera
- 机器学习: University of Washington
- 应用机器学习入门: Alberta Machine Intelligence Institute
- Advanced Machine Learning on Google Cloud: Google Cloud
- Applied Machine Learning and Model Optimization: Packt
- 金融领域的机器学习和强化学习: New York University
