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。

19 条评论

6 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。

19 条评论

6 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.

您将获得的技能

您将学习的工具

Python Programming

要了解的详细信息

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作业

7 项作业

授课语言：英语（English）

91%

of learners achieved a positive career outcome

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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。

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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65分钟

Introduction of Project4分钟
Overview of CNN5分钟
Installations and Dataset Structure11分钟
Import libraries7分钟
CNN Model and Layers Coding10分钟
Data Preprocessing and Augmentation7分钟
Understanding Data generator8分钟
Prediction on Single Image6分钟
Understanding Different Models and Accuracy6分钟

3个作业总计60分钟

Graded Quiz - Foundations of Convolutional Neural Networks30分钟
Getting Started with CNNs15分钟
Untitled15分钟

涵盖的内容

7个视频4个作业

7个视频总计63分钟

Introduction to Project7分钟
Google Collab7分钟
Importing Packages and Data8分钟
Preprocessing and Model Creation11分钟
Training the Model and Prediction11分钟
Model Creation using CNN11分钟
CNN Model Prediction9分钟

4个作业总计60分钟

Building Deep Learning with CNNs30分钟
Getting Started with the Project10分钟
Preparing Data and Models10分钟
Training, Testing, and CNN Mastery10分钟

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位教师

EDUCBA

1,366 门课程301,751 名学生

提供方

EDUCBA

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学生评论

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 25, 2025审阅

I went from CNN confusion to confidently building custom architectures in just a few weeks. The focus on practical debugging and common pitfalls was incredibly valuable.

已于 Jan 12, 2026审阅

The instructor’s expertise is evident in every lesson. Complex mathematical concepts are simplified into professional, actionable Python code that is easy to build and train

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.

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