What will you in A Complete Guide on TensorFlow 2.0 using Keras API Course
- Master TensorFlow 2 for deep learning and machine learning applications.
- Build and train neural networks using the Keras API.
- Apply CNNs, RNNs, and transfer learning to real-world datasets.
- Develop models for image classification, time series, and text generation.
- Explore advanced deployment options and TensorFlow tools like TensorBoard and TFLite.
Program Overview
Module 1: Introduction to TensorFlow 2
30 minutes
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Overview of TensorFlow and its role in deep learning.
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Setting up the development environment and installing TensorFlow.
Module 2: TensorFlow Basics and Keras API
45 minutes
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Understanding tensors, operations, and automatic differentiation.
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Building models using the Sequential and Functional APIs.
Module 3: Training Neural Networks
60 minutes
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Implementing loss functions, optimizers, and evaluation metrics.
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Training, validation, and testing workflows.
Module 4: Convolutional Neural Networks (CNNs)
60 minutes
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Designing and training CNNs for image classification tasks.
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Data augmentation, dropout, and batch normalization techniques.
Module 5: Recurrent Neural Networks (RNNs) and LSTMs
60 minutes
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Building RNNs for sequential and time series data.
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Using LSTMs and GRUs for more complex patterns.
Module 6: Natural Language Processing Projects
60 minutes
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Text preprocessing, tokenization, and word embeddings.
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Implementing models for text classification and generation.
Module 7: Transfer Learning and Pretrained Models
45 minutes
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Applying pretrained models like MobileNet and Inception.
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Fine-tuning vs. feature extraction.
Module 8: TensorFlow Tools and Deployment
45 minutes
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Using TensorBoard for tracking training progress.
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Saving models and deploying with TensorFlow Lite and TF Serving.
Module 9: Real-World Projects and Best Practices
75 minutes
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Complete ML/DL project implementation from data to deployment.
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Debugging, performance tuning, and production insights.
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Job Outlook
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High Demand: TensorFlow is a leading deep learning framework in industry and research.
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Career Advancement: Ideal for data scientists, AI engineers, and developers.
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Salary Potential: $110K–$170K+ for TensorFlow professionals with deployment skills.
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Freelance Opportunities: Projects in vision, NLP, and mobile AI apps.
Explore More Learning Paths
Advance your machine learning expertise by mastering TensorFlow and Keras — two of the most powerful tools in deep learning. These related courses will help you build, train, and deploy AI models that solve real-world problems efficiently.
Related Courses
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DeepLearning.AI TensorFlow Developer Professional Course — Gain practical experience building deep learning models with TensorFlow and prepare for the professional certification exam.
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TensorFlow: Data and Deployment Specialization Course — Learn to take your models from training to production with deployment techniques used in modern AI applications.
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TensorFlow: Advanced Techniques Specialization Course — Explore advanced model optimization, distributed training, and custom TensorFlow functions to refine your AI projects.
Related Reading
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What Is Data Management? — Discover how effective data organization and processing are fundamental to training accurate, high-performing machine learning models.
Last verified: March 12, 2026
