Machine Learning in Python: Analyze & Apply

本课程是 AI Machine Learning with R & Python Projects 专项课程的一部分

位教师：EDUCBA

访问权限由 New York State Department of Labor 提供

4个模块

深入了解一个主题并学习基础知识。

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

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 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

作业

16 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

了解关于 Coursera for Business 的更多信息

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 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

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.

This course provides a complete, hands-on pathway to mastering Python’s data science ecosystem. Each module balances conceptual clarity with practical coding examples, ensuring that learners not only understand theory but also build real-world skills. The inclusion of advanced topics like feature extraction, parameter tuning, and natural language processing sets this course apart from typical machine learning introductions. Whether you are a beginner in data science or a professional seeking to strengthen applied machine learning expertise, this course offers a structured, project-ready learning journey. Learners will leave with the confidence to build, validate, and deploy machine learning solutions across multiple domains.

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

14个视频总计122分钟

Introduction to Machine Learning 6分钟
Advantages and Disadvantages of Machine Learning 8分钟
NumPy Introduction 7分钟
Features and Installation 7分钟
NumPy Array Creation 10分钟
NumPy Array Attributes 8分钟
NumPy Array Operations 11分钟
NumPy Array Operations Continue 12分钟
NumPy Array Unary Operations 6分钟
Numpy Array Splicing 13分钟
NumPy Array Shpe 11分钟
Stacking Together Different Arrays 11分钟
Splitting one Array into Several Smaller ones 6分钟
Copies and Views 7分钟

4个作业总计60分钟

Introduction to Machine Learning 10分钟
Exploring NumPy Operations 10分钟
Advanced NumPy Techniques 10分钟
Graded-Foundations of Machine Learning and NumPy 30分钟

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

15个视频总计139分钟

NumPy Array Indexing 9分钟
NumPy Array Indexing Continue 6分钟
NumPy Array Boolean 10分钟
Introduction to Matlplotlib 5分钟
Understanding Various Functions of Pyplot 12分钟
Multiple Figures and Subplots 11分钟
Intro to Pandas 8分钟
Intro to Pandas Continue 8分钟
Data Structure in Pandas 11分钟
Data Structure in Pandas Continue 14分钟
Pandas Column Select 10分钟
Remove Operations 10分钟
Pandas Arithmetic Operations 12分钟
Pandas Arithmetic Operations Continue 7分钟
Introduction to Scikit Learn 8分钟

4个作业总计60分钟

NumPy Indexing and Boolean Operations 10分钟
Visualization with Matplotlib 10分钟
Mastering Pandas Operations 10分钟
Graded-Data Handling with NumPy, Matplotlib, and Pandas 30分钟

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

13个视频总计135分钟

Supervised 9分钟
Unsupervised Learning 8分钟
Load Data Set 6分钟
Scikit Example Digits 7分钟
Digits Dataset Using Matplotlib 7分钟
Understading Metrics of Predicted Digits Dataset 6分钟
Persisting Models 14分钟
K-NN Algorithm with Example 15分钟
Cross Validation 14分钟
Cross Validation Techniques 7分钟
K-Means Clustering Example 15分钟
Agglomeration 11分钟
PCA Pipeline 16分钟

4个作业总计60分钟

Fundamentals of Machine Learning Models 10分钟
Model Evaluation and Validation 10分钟
Clustering and Dimensionality Reduction 10分钟
Graded-Supervised and Unsupervised Learning with Scikit-Learn 30分钟

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.