What you'll get
- 9+ Hours
- 4 Courses
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- Create, train, and deploy neural network models using the Keras framework.
- Implement deep learning–based unsupervised learning techniques with Keras.
- Apply supervised deep learning approaches for predictive modeling.
- Design and build convolutional neural networks (CNNs) to solve real-world problems.
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 course provides a structured, hands-on introduction to machine learning and deep learning with Keras. It provides learners with the core expertise and hands-on capabilities needed to use the Keras framework effectively, without the need for extra tools or supplemental training. As Python continues to dominate large-scale data analysis and artificial intelligence initiatives, Keras has become a central component of modern deep learning pipelines.
Through guided instruction and practical exercises, participants learn how to design, train, and evaluate a variety of neural network architectures. All development takes place in an online environment, removing the need for local software installation. The course includes sample code, guided practice activities, and four applied projects that demonstrate different network designs using real-world datasets. While prior exposure to deep learning concepts is helpful, only a fundamental understanding is required, making the program both accessible and comprehensive.
Goals
- Enable learners to confidently use Keras for building deep learning models.
- Develop practical skills in designing neural networks for diverse use cases.
- Strengthen understanding of supervised and unsupervised deep learning methods.
- Prepare participants to apply deep learning techniques in professional data science projects.
Objectives
By the end of this course, learners will be able to:
- Understand the core concepts of machine learning and deep learning.
- Build and train neural networks using the Keras API.
- Apply unsupervised learning techniques driven by deep learning models.
- Implement supervised learning solutions for classification and prediction tasks.
- Design and deploy convolutional neural networks for image and pattern recognition problems.
Highlights
- End-to-end coverage of deep learning with Keras.
- Fully online coding environment with no local setup required.
- Step-by-step guided lessons and hands-on exercises.
- Four practical projects using real-world datasets.
- Focus on industry-relevant architectures and workflows.
- Beginner-friendly structure with progressive skill development.
Requirements
- Ability to install and run basic software on a computer.
- Prior experience with Python programming is beneficial.
- Fundamental knowledge of statistical principles and data analysis techniques.
- Familiarity with common machine learning terms, such as training, validation, and cross-validation, is recommended.
Target Audience
- Individuals interested in applying TensorFlow and Keras to Python-based data science projects.
- Learners with prior exposure to Python or foundational data science concepts.
- Aspiring data scientists, machine learning practitioners, and AI enthusiasts.
- Professionals looking to explore or transition into deep learning and neural network development.
FAQ
Q1. Is prior deep learning experience required?
No. While prior exposure is helpful, the course is designed to support learners with a basic understanding of Python and data science fundamentals.
No. While prior exposure is helpful, the course is designed to support learners with a basic understanding of Python and data science fundamentals.
Q2. Do learners need to install any software locally?
No. All coding and exercises are completed in an online environment.
No. All coding and exercises are completed in an online environment.
Q3. What tools and frameworks are covered?
The course focuses on Keras, with relevant integration into TensorFlow-based workflows.
The course focuses on Keras, with relevant integration into TensorFlow-based workflows.
Q4. Are there practical projects included?
Yes. Learners complete four hands-on projects that demonstrate different neural network architectures and real-world applications.
Yes. Learners complete four hands-on projects that demonstrate different neural network architectures and real-world applications.
Q5. Will this course help with real-world job requirements?
Yes. The curriculum emphasizes practical skills and industry-aligned deep learning use cases.
Yes. The curriculum emphasizes practical skills and industry-aligned deep learning use cases.
Career Benefits
- Builds job-ready skills in deep learning and neural network development.
- Enhances proficiency in Keras, a widely used industry framework.
- Supports career growth in data science, machine learning, and AI roles.
- Enables professionals to contribute effectively to AI-driven projects.
- Strengthens resumes with practical project experience in deep learning.
