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：新状态：免费试用您将获得的技能: 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：免费试用University of Michigan
您将获得的技能: Model Evaluation, 预测建模, 无监督学习, 回归分析, Scikit-learn （机器学习库）, 监督学习, 人工神经网络, Python 程序设计, 分类算法, 随机森林算法, 功能工程, 决策树学习, 应用机器学习, 机器学习
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：新状态：免费试用您将获得的技能: 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
状态：免费试用多位教师
您将获得的技能: 人工智能, Model Evaluation, 预测建模, 深度学习, 数据伦理, 监督学习, 无监督学习, NumPy, 张力流, 分类算法, Jupyter, 数据预处理, 强化学习, Scikit-learn （机器学习库）, 功能工程, 随机森林算法, 应用机器学习, Transfer Learning, 机器学习, 决策树学习
状态：免费试用DeepLearning.AI
您将获得的技能: 人工智能, Model Evaluation, 预测建模, 逻辑回归, 无监督学习, 回归分析, Python 程序设计, 监督学习, NumPy, 分类算法, 数据预处理, Jupyter, Scikit-learn （机器学习库）, 功能工程, 机器学习
是什么让您今天来到 Coursera？
状态：免费试用您将获得的技能: 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
状态：免费试用Alberta Machine Intelligence Institute
您将获得的技能: 机器学习算法, 无监督学习, 数据伦理, 数据收集, 人工智能, 监督学习, 业务要求, 案例研究, 应用机器学习, 产品生命周期管理, 机器学习, 数据预处理, 商业分析, 数据质量
状态：免费试用您将获得的技能: Transfer Learning, Data Preprocessing, Hugging Face, Model Evaluation
状态：免费试用University of Washington
您将获得的技能: 逻辑回归, 计算机视觉, Model Evaluation, 深度学习, 预测建模, 贝叶斯统计, 无监督学习, 人工智能, 统计建模, 回归分析, 监督学习, 统计机器学习, 数据预处理, 分类算法, 功能工程, 决策树学习, 应用机器学习, 数据挖掘, 机器学习, 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: Model Evaluation, 统计方法, 预测建模, 监督学习, 无监督学习, 衍生产品, 张力流, 深度学习, 回归分析, 降维, 强化学习, 市场流动性, Scikit-learn （机器学习库）, 人工神经网络, 应用机器学习, 机器学习, 金融市场, 马尔可夫模型, 金融交易, 决策树学习
是什么让您今天来到 Coursera？
总之，以下是 10 最受欢迎的 applied machine learning 课程
- Applied Machine Learning Without Coding: Edureka
- 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
- 机器学习: DeepLearning.AI
- 监督式机器学习：回归与分类: DeepLearning.AI
- Machine Learning with Scikit-learn, PyTorch & Hugging Face: Coursera
- 应用机器学习入门: Alberta Machine Intelligence Institute
- Advanced Machine Learning Techniques: Coursera
- 机器学习: University of Washington
