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.
Deep Learning with Python: CNN, ANN & RNN Specialization
Deep Learning with Python: CNN, ANN & RNN Specialization
Build Neural Networks for Real AI Projects. Master CNN, ANN, and RNN in Python with hands-on projects and real-world case studies
Instructor: EDUCBA
Included with 
Get in-depth knowledge of a subject
Beginner level
Recommended experience
1 month at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
Get in-depth knowledge of a subject
Beginner level
Recommended experience
1 month at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
What you'll learn
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.
Overview
What’s included
Shareable certificate
Add to your LinkedIn profile
Taught in English
Recently updated!
October 2025
20 practice exercises
Advance your subject-matter expertise
- Learn in-demand skills from university and industry experts
- Master a subject or tool with hands-on projects
- Develop a deep understanding of key concepts
- Earn a career certificate from EDUCBA
Specialization - 3 course series
What you'll learn
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.
Skills you'll gain
Data Processing, Image Analysis, Applied Machine Learning, Development Environment, Artificial Intelligence and Machine Learning (AI/ML), Python Programming, Keras (Neural Network Library), Tensorflow, Deep Learning, Google Cloud Platform, Computer Vision, and Artificial Neural Networks
What you'll learn
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.
Skills you'll gain
Data Cleansing, Feature Engineering, Exploratory Data Analysis, Tensorflow, Deep Learning, Data Processing, Predictive Modeling, Customer Analysis, Software Installation, Artificial Neural Networks, Keras (Neural Network Library), and Predictive Analytics
What you'll learn
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.
Skills you'll gain
Financial Forecasting, Artificial Neural Networks, Exploratory Data Analysis, Matplotlib, Pandas (Python Package), Keras (Neural Network Library), Data Visualization, Deep Learning, NumPy, Tensorflow, Feature Engineering, Data Processing, Predictive Modeling, Data Transformation, and Time Series Analysis and Forecasting
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
Compare with similar products
| Rating | ||||
|---|---|---|---|---|
| Level | ||||
| Skills | ||||
| Last updated | ||||
| Number of practice exercises | ||||
| Degree eligibility | ||||
| Part of Coursera Plus |
You might also like
Why people choose Coursera for their career
Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."
Open new doors with Coursera Plus
Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription
Advance your career with an online degree
Earn a degree from world-class universities - 100% online
Join over 3,400 global companies that choose Coursera for Business
Upskill your employees to excel in the digital economy
Frequently asked questions
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.
More questions
Financial aid available,