By the end of this course, learners will be able to analyze machine learning fundamentals, apply NumPy for numerical computing, visualize data with Matplotlib, and manage structured datasets using Pandas. They will also be able to evaluate supervised and unsupervised models in scikit-learn, optimize performance through validation techniques, and implement advanced applications such as face recognition, text classification, and sentiment analysis.
Machine Learning in Python: Analyze & Apply
您将学到什么
Apply NumPy, Pandas, and Matplotlib for data analysis & visualization.
Build, train, and validate supervised & unsupervised ML models.
Implement NLP, face recognition, and text classification projects.
您将获得的技能
要了解的详细信息
可分享的证书
添加到您的领英档案
最近已更新！
October 2025
作业
16 项作业
授课语言：英语（English）
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积累特定领域的专业知识
本课程是 AI Machine Learning with R & Python Projects 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4个模块
This module introduces the core concepts of machine learning and the fundamental role of NumPy in Python-based data science. Learners explore the advantages and challenges of machine learning, install and set up NumPy, and perform basic array operations. By the end, students gain a solid foundation for working with numerical data structures in Python.
涵盖的内容
14个视频4个作业
This module focuses on data manipulation and visualization using Python’s scientific libraries. Learners advance their NumPy skills with indexing and Boolean operations, visualize data through Matplotlib plots, and master structured data handling with Pandas. These tools form the backbone of efficient exploratory data analysis.
涵盖的内容
15个视频4个作业
This module introduces machine learning models through scikit-learn, covering both supervised and unsupervised approaches. Learners explore datasets, train classifiers, validate models with cross-validation, and evaluate performance metrics. By the end, they understand clustering, dimensionality reduction, and core ML workflows.
涵盖的内容
13个视频4个作业
This module covers advanced applications of machine learning, including face recognition, text classification, and natural language processing. Learners extract features, train classifiers, tune parameters, and conduct sentiment analysis. The skills gained prepare students to apply machine learning in real-world contexts.
涵盖的内容
12个视频4个作业
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When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
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