Is this course really 100% online? Do I need to attend any classes in person?

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

Can I just enroll in a single course?

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

Is financial aid available?

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.

Can I take the course for free?

No, you cannot take this course for free. 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. If you cannot afford the fee, you can apply for financial aid.

Will I earn university credit for completing the Specialization?

This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

Deep Learning with Python: CNN, ANN & RNN 专项课程

即将结束： 只需 199 美元（原价 399 美元）即可通过 Coursera Plus 学习新技能。立即节省

Deep Learning with Python: CNN, ANN & RNN 专项课程

Build Neural Networks for Real AI Projects. Master CNN, ANN, and RNN in Python with hands-on projects and real-world case studies

位教师：EDUCBA

包含在中

了解更多

3 门课程系列

深入学习学科知识

4.5

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初级等级

推荐体验

4 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

3 门课程系列

深入学习学科知识

4.5

（6 条评论）

初级等级

推荐体验

4 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

Design, build, and evaluate CNN, ANN, and RNN models in Python using TensorFlow and Keras.
Apply preprocessing, feature engineering, and optimization techniques to real-world datasets.
Implement deep learning solutions for image recognition, customer churn, and stock forecasting.

您将获得的技能

您将学习的工具

要了解的详细信息

可分享的证书

添加到您的领英档案

授课语言：英语（English）

了解顶级公司的员工如何掌握热门技能

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

精进特定领域的专业知识

向大学和行业专家学习热门技能
借助实践项目精通一门科目或一个工具
培养对关键概念的深入理解
通过 EDUCBA 获得职业证书

专业化 - 3门课程系列

This Specialization provides a practical, project-driven pathway to mastering deep learning with Python. Learners will explore Convolutional Neural Networks (CNNs), Artificial Neural Networks (ANNs), and Recurrent Neural Networks (RNNs) with LSTM layers through real-world case studies in image recognition, customer churn prediction, and stock price forecasting. Each course emphasizes both theory and hands-on coding using TensorFlow and Keras, ensuring you graduate with job-ready AI skills and the ability to apply neural networks to authentic business and financial problems.

应用的学习项目

Learners will complete guided projects that apply deep learning to real datasets, including image classification, customer churn analysis, and stock price forecasting. These projects ensure practical application of Python-based neural networks, building confidence to solve real-world AI challenges.

Master CNNs with Python: Build, Train & Evaluate Models

第 1 门课程 4小时

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

类别：Convolutional Neural Networks

类别：Data Preprocessing

类别：Keras (Neural Network Library)

类别：Deep Learning

类别：Computer Vision

类别：Model Evaluation

类别：Cloud Computing

类别：Python Programming

类别：Development Environment

类别：Tensorflow

类别：Image Analysis

Deep Learning with ANN in Python: Build & Optimize

第 2 门课程 4小时

您将学到什么

Configure Python environments and preprocess structured data.
Build, train, and optimize ANN models with TensorFlow & Keras.
Handle imbalanced datasets and apply ANN to churn prediction.

您将获得的技能

类别：Artificial Neural Networks

类别：Data Preprocessing

类别：Deep Learning

类别：Development Environment

类别：Model Evaluation

类别：Tensorflow

类别：Customer Analysis

类别：Data Cleansing

类别：Scikit Learn (Machine Learning Library)

类别：Applied Machine Learning

类别：Pandas (Python Package)

类别：Predictive Analytics

类别：Keras (Neural Network Library)

Deep Learning RNN & LSTM: Stock Price Prediction

第 3 门课程 4小时

您将学到什么

Preprocess stock datasets with feature scaling and EDA.
Build and train RNNs with LSTM layers for time-series data.
Evaluate and visualize stock predictions using real datasets.

您将获得的技能

类别：Recurrent Neural Networks (RNNs)

类别：Model Evaluation

类别：Exploratory Data Analysis

类别：Time Series Analysis and Forecasting

类别：Data Preprocessing

类别：Data Visualization

类别：Data Transformation

类别：Deep Learning

类别：Financial Forecasting

类别：Data Manipulation

类别：Pandas (Python Package)

类别：NumPy

类别：Keras (Neural Network Library)

获得职业证书

将此证书添加到您的 LinkedIn 个人资料、简历或履历中。在社交媒体和绩效考核中分享。

位教师

EDUCBA

683 门课程 193,352 名学生

提供方

EDUCBA

人们为什么选择 Coursera 来帮助自己实现职业发展

Felipe M.

自 2018开始学习的学生

''能够按照自己的速度和节奏学习课程是一次很棒的经历。只要符合自己的时间表和心情，我就可以学习。'

Jennifer J.

自 2020开始学习的学生

''我直接将从课程中学到的概念和技能应用到一个令人兴奋的新工作项目中。'

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自 2021开始学习的学生

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常见问题

Learners can expect to complete this Specialization in approximately 5 to 6 weeks with a dedicated study time of 3–4 hours per week. The flexible, self-paced structure allows you to balance learning with your personal or professional commitments, while still progressing through hands-on projects and case studies that reinforce practical deep learning skills. By the end, you will have developed the ability to confidently apply CNNs, ANNs, and RNNs to real-world problems using Python.

A foundational understanding of Python programming, statistics, and basic machine learning concepts is recommended. Familiarity with data preprocessing and linear algebra will also help learners maximize their experience.

Yes. The courses are designed to build progressively, starting with ANN fundamentals, then advancing to CNNs, and finally exploring RNNs with LSTM for sequential data. Following the order ensures you develop a solid foundation before tackling more complex architectures.

By completing this Specialization, you will be able to design, train, and evaluate deep learning models using Python. You will gain hands-on experience with CNNs for image tasks, ANNs for predictive modeling, and RNNs with LSTMs for time-series forecasting. These skills will empower you to apply neural networks to practical business and AI-driven projects.

Deep Learning with Python: CNN, ANN & RNN 专项课程

Deep Learning with Python: CNN, ANN & RNN 专项课程

您将学到什么

您将获得的技能

您将学习的工具

要了解的详细信息

了解顶级公司的员工如何掌握热门技能

精进特定领域的专业知识

专业化 - 3门课程系列

Master CNNs with Python: Build, Train & Evaluate Models

您将学到什么

您将获得的技能

Deep Learning with ANN in Python: Build & Optimize

您将学到什么

您将获得的技能

Deep Learning RNN & LSTM: Stock Price Prediction

您将学到什么

您将获得的技能

获得职业证书

位教师

提供方

人们为什么选择 Coursera 来帮助自己实现职业发展

更高阶技能，新年优惠。

推动业务发展，增强团队能力

常见问题

更多问题

Deep Learning with Python: CNN, ANN & RNN 专项课程

Deep Learning with Python: CNN, ANN & RNN 专项课程

您将学到什么

您将获得的技能

您将学习的工具

要了解的详细信息

了解顶级公司的员工如何掌握热门技能

精进特定领域的专业知识

专业化 - 3门课程系列

Master CNNs with Python: Build, Train & Evaluate Models

您将学到什么

您将获得的技能

Deep Learning with ANN in Python: Build & Optimize

您将学到什么

您将获得的技能

Deep Learning RNN & LSTM: Stock Price Prediction

您将学到什么

您将获得的技能

获得职业证书

位教师

提供方

人们为什么选择 Coursera 来帮助自己实现职业发展

更高阶技能，新年优惠。

推动业务发展，增强团队能力

常见问题

How long does it take to complete the Specialization?

What background knowledge is necessary?

Do I need to take the courses in a specific order?

更多问题