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Machine Learning with Implementation in Java
Board Infinity

Machine Learning with Implementation in Java

本课程是 Java in Machine Learning 专项课程 的一部分

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位教师:Board Infinity

包含在 Coursera Plus

深入了解一个主题并学习基础知识。
中级 等级

推荐体验

1 周 完成
在 10 小时 一周
灵活的计划
自行安排学习进度
深入了解一个主题并学习基础知识。
中级 等级

推荐体验

1 周 完成
在 10 小时 一周
灵活的计划
自行安排学习进度

您将学到什么

  • Apply data preprocessing techniques using Java tools like Weka and Tribuo for machine learning tasks.

  • Build, train, and evaluate classification, regression, and deep learning models using DL4J, Tribuo, and DJL.

  • Implement NLP and scalable machine learning workflows using Apache OpenNLP, Spark MLlib, and Mahout.

  • Deploy machine learning models using standardized formats like PMML and ONNX, ensuring cross-platform interoperability and production readiness.

要了解的详细信息

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最近已更新!

June 2025

作业

12 项作业

授课语言:英语(English)

了解顶级公司的员工如何掌握热门技能

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 Java in Machine Learning 专项课程 专项课程的一部分
在注册此课程时,您还会同时注册此专项课程。
  • 向行业专家学习新概念
  • 获得对主题或工具的基础理解
  • 通过实践项目培养工作相关技能
  • 获得可共享的职业证书

该课程共有3个模块

Data Handling & Preprocessing with Java focuses on the essential first step of any machine learning pipeline—preparing data for model training. This module introduces learners to key concepts such as data cleaning, normalization, feature selection, and transformation, all within the context of Java-based development. Using libraries like Weka and Tribuo, learners will gain practical experience in managing datasets, handling missing values, encoding categorical variables, and scaling features. The module emphasizes the importance of high-quality input data and walks through end-to-end preprocessing workflows tailored to real-world Java applications. By mastering these techniques, learners will be equipped to build reliable, accurate machine learning models that are grounded in well-structured, meaningful data.

涵盖的内容

8个视频4篇阅读材料4个作业1个讨论话题2个插件

Deep Learning in Java introduces learners to the fundamentals of deep learning and demonstrates how to build and deploy neural networks using Java-based frameworks. This module begins by explaining key concepts such as artificial neurons, activation functions, backpropagation, and multi-layer architectures. Learners will explore how deep learning differs from traditional machine learning, and where it excels—especially in tasks involving images, text, and complex data patterns. The hands-on portion of the module focuses on building and training deep learning models using libraries like DeepLearning4J (DL4J), covering tasks such as image classification and sentiment analysis. Learners will also learn how to fine-tune models, manage training processes, and evaluate model performance. By the end of this module, learners will have the confidence to apply deep learning in real-world Java applications.

涵盖的内容

10个视频3篇阅读材料4个作业1个插件

Specialized Libraries & Techniques explores advanced tools and strategies that extend the capabilities of machine learning in Java. This module introduces learners to a variety of specialized Java libraries designed for specific tasks such as natural language processing (NLP), time series forecasting, and reinforcement learning. Learners will gain hands-on experience with tools like ND4J for numerical computing, Smile for statistical learning, and Stanford CoreNLP for text analysis. In addition to tool-based learning, this module covers advanced ML techniques such as hyperparameter tuning, ensemble modeling, and model serialization. The focus is on equipping learners with a broader toolkit and deeper insight into solving complex problems efficiently and effectively within Java environments.

涵盖的内容

10个视频3篇阅读材料4个作业1个插件

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位教师

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