| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Beginner |
| Duration | 5 weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹7499 / $107 |
| Tools | TensorFlow |
About the TensorFlow and Keras Basics Course
TensorFlow - Use in AI is a comprehensive course tailored for M.Tech, M.Sc, and MCA students, as well as professionals in the fields of IT, BFSI, consulting, and fintech.
The course covers foundational concepts to advanced applications of TensorFlow in AI, emphasizing hands-on learning and real-world problem-solving.
Program Highlights
• Comprehensive coverage of TensorFlow and Keras Basics from fundamentals to advanced applications
• Hands-on projects and real-world case studies in Science & Technology
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Exposure to industry-standard tools and platforms used in Science & Technology
• Career-oriented training for academic and professional growth in Science & Technology
Course Curriculum
Module 1: Introduction to TensorFlow and Keras Section 1.1: Introduction to TensorFlow
- Subsection 1.1.1: What is TensorFlow? Overview of TensorFlow as a deep learning framework.
- Benefits of using TensorFlow: Flexibility, scalability, and performance.
Module 2: Core Concepts of TensorFlow and Keras Section 2.1: Tensors in TensorFlow
- Subsection 2.1.1: Understanding Tensors What is a tensor? Types of tensors in TensorFlow.
- Operations on tensors: addition, multiplication, and reshaping.
- TensorFlow tensor objects vs. NumPy arrays.
Module 3: Advanced Techniques with TensorFlow and Keras Section 3.1: Model Overfitting and Regularization
- Subsection 3.1.1: Overfitting in Neural Networks What is overfitting, and how it impacts model performance.
- Symptoms of overfitting and underfitting in machine learning models.
Module 4: Model Evaluation and Fine-Tuning Section 4.1: Model Evaluation and Hyperparameter Tuning
- Subsection 4.1.1: Cross-validation Techniques What is cross-validation?
- Implementing K-fold cross-validation in TensorFlow.
- Evaluating model stability using cross-validation.
Tools, Techniques, or Platforms Covered
TensorFlow
Real-World Applications
- Apply TensorFlow and Keras Basics skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical Science & Technology competencies
- Solve industry-relevant problems using TensorFlow and Keras Basics methodologies and tools
- Contribute to open-source projects and collaborative research in Science & Technology
- Prepare for competitive examinations, interviews, and professional certifications in Science & Technology
Who Should Attend & Prerequisites
- Students pursuing degrees in Science & Technology, science, engineering, or related disciplines
- Working professionals seeking to upskill or transition into Science & Technology roles
- Researchers and academicians looking to adopt modern techniques in Science & Technology
- Entrepreneurs, freelancers, and self-learners interested in practical Science & Technology knowledge
Certification
