Johns Hopkins University
Applied Machine Learning 专项课程
Johns Hopkins University

Applied Machine Learning 专项课程

Master Applied Machine Learning Techniques. Master advanced machine learning techniques to solve real-world problems in data processing, computer vision, and neural networks

Erhan Guven

位教师:Erhan Guven

包含在 Coursera Plus

深入学习学科知识
3.6

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推荐体验

12 周 完成
在 5 小时 一周
灵活的计划
自行安排学习进度
深入学习学科知识
3.6

(10 条评论)

中级 等级

推荐体验

12 周 完成
在 5 小时 一周
灵活的计划
自行安排学习进度

您将学到什么

  • Master data preprocessing techniques for machine learning applications.

  • Evaluate and optimize machine learning models for performance and accuracy.

  • Implement supervised and unsupervised learning algorithms effectively.

  • Apply advanced neural network architectures like Convolutional Neural Networks (CNNs) in computer vision tasks.

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授课语言:英语(English)

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精进特定领域的专业知识

  • 向大学和行业专家学习热门技能
  • 借助实践项目精通一门科目或一个工具
  • 培养对关键概念的深入理解
  • 通过 Johns Hopkins University 获得职业证书

专业化 - 3门课程系列

您将学到什么

  • Understand and implement machine learning techniques for computer vision tasks, including image recognition and object detection.

  • Analyze data features and evaluate machine learning model performance using appropriate metrics and evaluation techniques.

  • Apply data pre-processing methods to clean, transform, and prepare data for effective machine learning model training.

  • Implement and optimize supervised learning algorithms for classification and regression tasks.

您将获得的技能

类别:Applied Machine Learning
类别:Supervised Learning
类别:Feature Engineering
类别:Machine Learning Algorithms
类别:Data Transformation
类别:Machine Learning
类别:Data Cleansing
类别:Scikit Learn (Machine Learning Library)
类别:Predictive Modeling
类别:Data Analysis
类别:Image Analysis
类别:Computer Vision

您将学到什么

  • Understand and apply ensemble methods to improve model accuracy and robustness by combining multiple learning algorithms.

  • Explore advanced regression techniques for predicting continuous outcomes and modeling complex relationships in data.

  • Apply unsupervised learning algorithms for clustering, dimensionality reduction, and pattern recognition in unlabeled data.

  • Understand and implement reinforcement learning techniques and apriori analysis for decision-making and association rule mining.

您将获得的技能

类别:Reinforcement Learning
类别:Supervised Learning
类别:Predictive Modeling
类别:Regression Analysis
类别:Machine Learning Algorithms
类别:Random Forest Algorithm
类别:Unsupervised Learning
类别:Decision Tree Learning
类别:Dimensionality Reduction
类别:Statistical Machine Learning
类别:Advanced Analytics
类别:Applied Machine Learning
类别:Machine Learning
类别:Data Mining

您将学到什么

  • Build neural networks from scratch and apply them to real-world datasets like MNIST.

  • Apply back-propagation for optimizing neural network models and understand computational graphs.

  • Utilize L1, L2, drop-out regularization, and decision tree pruning to reduce model overfitting.

  • Implement convolutional neural networks (CNNs) and tensors using PyTorch for image and audio processing.

您将获得的技能

类别:Artificial Neural Networks
类别:Deep Learning
类别:PyTorch (Machine Learning Library)
类别:Network Architecture
类别:Computer Vision
类别:Decision Tree Learning
类别:Supervised Learning
类别:Machine Learning Algorithms
类别:Machine Learning
类别:Performance Tuning

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位教师

Erhan Guven
Johns Hopkins University
3 门课程1,992 名学生

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