What you'll get
- 9+ Hours
- 4 Courses
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- Build and apply neural network models using Keras.
- Use Keras to perform deep learning–driven unsupervised learning techniques.
- Apply supervised learning methods powered by deep learning in Keras.
- Develop and implement convolutional neural networks (CNNs) using Keras.
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Project on Keras: Building a Chatbot | 4h 9m | ✔ | View Curriculum |
| Project On Keras: Sentimental Analysis using RNN | 1h 15m | ✔ | View Curriculum |
| Project on Keras: Image Classification | 1h 36m | ✔ | View Curriculum |
| Creating An Advanced Face Recognition Computer Vision App | 3h 12m | ✔ | View Curriculum |
Description
This program offers a comprehensive, practical introduction to machine learning and deep learning with the Keras framework. It prepares learners with the core competencies needed to work effectively with Keras, eliminating the need for additional training or supplementary resources. As organizations turn to Python for large-scale data analysis, Keras plays a key role in modern deep learning workflows. Mastery of this framework enables learners to contribute more effectively to their teams and advance their professional development.
The program features guided lessons and hands-on exercises that teach learners to design, develop, and train various neural network models with Keras. All coding is completed online, so no local setup is necessary. Learners receive example scripts, practice tasks, and four projects that showcase different architectures and real-world datasets. While prior knowledge of deep learning is helpful, only a basic foundation is required. This structured approach ensures participants gain the skills to design and implement neural networks confidently using Keras.
Requirements
- Should be able to install and operate software on a computer.
- Previous experience with Python-based data science is advantageous.
- A foundational grasp of statistics and how to apply those principles can be beneficial.
- Familiarity with common machine learning terminology, such as cross-validation, is recommended.
Target Audience
- This material is intended for those interested in using TensorFlow and Keras for Python-based data science projects.
- It is suitable for learners with prior experience in Python programming or basic data science concepts.
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Anyone eager to build and experiment with neural networks and deep learning models using the Keras framework.
