TensorFlow is an open-source machine learning framework used to build, train and deploy AI models for many applications. Our TensorFlow Training offers step-by-step learning through video tutorials and live sessions. You will work on hands-on projects to develop and optimize deep learning models. The course equips you to apply TensorFlow for various AI challenges and prepares you for related industry certifications and roles.
Objectives of the Course
- Understand deep learning basics with TensorFlow and neural network principles
- Get hands-on experience setting up and using TensorFlow
- Build, train, and fine-tune deep learning models, including CNNs and RNNs
- Implement model optimization and regularization techniques
- Deploy models for practical AI applications
- Gain confidence to pursue TensorFlow-related certifications and projects
Pre-requisites
- Basic programming skills in Python
- Understanding of machine learning fundamentals
- Familiarity with linear algebra and calculus concepts is helpful but not mandatory
Target Audience
- Data Scientists and Analysts
- Machine Learning Engineers
- AI Enthusiasts and Researchers
- Software Developers looking to work with AI
- Students and Professionals aiming to build expertise in deep learning
- Anyone interested in implementing neural networks using TensorFlow
What You Will Learn
- Introduction to TensorFlow and its Ecosystem
- Install TensorFlow
- Basics of Neural Networks
- Data Preprocessing and Handling
- Building Deep Learning Models
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Model Training and Optimization
- Model Evaluation and Performance Tuning
- Using Keras with TensorFlow
- Working with TensorFlow Hub and Pretrained Models
- Deploying Models for Real-World Applications
- TensorFlow Datasets (tf.data)
- Transfer Learning
- Custom Training Loops
- TensorBoard
- Saving and Loading Models
- TensorFlow Lite (mobile deployment)
- TensorFlow Serving
- Distributed Training (basic concepts)
- Advanced Deep Learning Concepts
Job Roles After TensorFlow Certification Training
- Machine Learning Engineer
- Deep Learning Engineer
- AI Engineer
- Data Scientist
- Computer Vision Engineer
- NLP Engineer
- AI Research Engineer
- Software Engineer (AI/ML)
- MLOps Engineer
Salary Expectations
United States
- AI/ML Engineer: $70,000 – $90,000 per year
- Machine Learning Developer: $80,000 – $110,000 per year
- Data Scientist / Deep Learning Specialist: $90,000 – $130,000 per year
- Senior AI Engineer / ML Architect: $120,000 – $160,000+ per year
Source: Indeed
India
- AI/ML Engineer: ₹6 – 12 LPA
- Machine Learning Developer:₹8 – 18 LPA
- Data Scientist / Deep Learning Specialist: ₹10 – 25 LPA
- Senior AI Engineer / ML Architect: ₹20 – 40 LPA
Source: Glassdoor India
TensorFlow expertise significantly enhances career growth and opens opportunities in top tech companies, startups, and research-based organizations.
