Master CNNs with Python: Build, Train & Evaluate Models

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Master CNNs with Python: Build, Train & Evaluate Models

本课程是 Deep Learning with Python: CNN, ANN & RNN 专项课程的一部分

位教师：EDUCBA

包含在中

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2个模块

深入了解一个主题并学习基础知识。

4.7

（19 条评论）

4 小时完成

灵活的计划

自行安排学习进度

2个模块

深入了解一个主题并学习基础知识。

4.7

（19 条评论）

4 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Explain CNN fundamentals and apply Python for model building.
Preprocess and augment image datasets for training workflows.
Design, implement, and evaluate CNNs for image classification.

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积累特定领域的专业知识

本课程是 Deep Learning with Python: CNN, ANN & RNN 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
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该课程共有2个模块

By the end of this course, learners will be able to design, build, train, and evaluate Convolutional Neural Networks (CNNs) using Python, gaining hands-on experience in one of the most in-demand deep learning skills. You will learn to set up both local and cloud-based environments, preprocess and augment image datasets, implement CNN architectures, and assess model accuracy and performance.

Through structured lessons, coding exercises, and real-world projects, you’ll develop not only the theoretical foundation but also the practical ability to apply CNNs to tasks like image classification. Each concept is reinforced with quizzes and guided implementations, ensuring immediate feedback and skill mastery. What makes this course unique is its project-driven, modular approach—every step from data preparation to prediction workflows is directly tied to Python code, with clear, reproducible results. Whether you’re new to deep learning or transitioning from basic machine learning, this course equips you with job-ready CNN skills to confidently tackle modern AI challenges.

This module introduces learners to the essential foundations of Convolutional Neural Networks (CNNs) in Python, covering project setup, CNN architecture, coding, data preprocessing, and model evaluation. By the end, learners will be equipped to design, implement, and test CNN models for real-world image classification tasks.

涵盖的内容

9个视频3个作业

9个视频总计64分钟

Introduction of Project 4分钟
Overview of CNN 4分钟
Installations and Dataset Structure 11分钟
Import libraries 7分钟
CNN Model and Layers Coding 10分钟
Data Preprocessing and Augmentation 6分钟
Understanding Data generator 8分钟
Prediction on Single Image 5分钟
Understanding Different Models and Accuracy 6分钟

3个作业总计60分钟

Graded Quiz - Foundations of Convolutional Neural Networks 30分钟
Getting Started with CNNs 15分钟
Untitled 15分钟

涵盖的内容

7个视频4个作业

7个视频总计62分钟

Introduction to Project 6分钟
Google Collab 7分钟
Importing Packages and Data 7分钟
Preprocessing and Model Creation 10分钟
Training the Model and Prediction 10分钟
Model Creation using CNN 10分钟
CNN Model Prediction 8分钟

4个作业总计60分钟

Building Deep Learning with CNNs 30分钟
Getting Started with the Project 10分钟
Preparing Data and Models 10分钟
Training, Testing, and CNN Mastery 10分钟

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位教师

EDUCBA

683 门课程 193,352 名学生

提供方

EDUCBA

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学生评论

4.7

19 条评论

5 stars
78.94%
4 stars
15.78%
3 stars
0%
2 stars
5.26%
1 star
0%

显示 3/19 个

已于 Dec 27, 2025审阅

Beginner-friendly course on CNNs. It helped me understand architecture design, model training, and evaluation with confidence.

已于 Jan 4, 2026审阅

From theory to deployment-ready models — this course covers the full lifecycle of professional CNN development exceptionally well.

已于 Jan 10, 2026审阅

Exceptional depth without confusion; perfect for mastering CNN training and optimization techniques.

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