TensorFlow Training Online and Certification Course

12 Lesson

40 Hours

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igmGuru offers the best TensorFlow Training online worldwide. This comprehensive TensorFlow program covers basic to advanced key topics such as building and training deep learning models, working with neural networks, implementing computer vision, natural language processing, and deploying AI models into production. Our course modules are designed by our industry experts with over 15 years of experience in artificial intelligence and machine learning. Also, the modules are aligned with the latest TensorFlow 2.20 version. Enroll in the TensorFlow course today to prepare for the latest TensorFlow developer certification exam and become certified.

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TensorFlow Course Overview

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
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.

Key Features

Instructor Led Training : 40 Hrs
Experienced & Professional Trainers
Small Batches Upto 10 Participants
24 X 7 Lifetime Support & Access
1 On 1 Training Option Available
Flexible Schedule

TensorFlow Training Modules

Lesson 1 - Introduction to Machine Learning & TensorFlow

1. Overview of Machine Learning concepts

2. Types of Machine Learning (Supervised, Unsupervised)

3. Introduction to Deep Learning

4. What is TensorFlow and its architecture

5. TensorFlow ecosystem and applications

6. Setting up TensorFlow environment

Lesson 2 - TensorFlow Basics

1. Tensors, constants, and variables

2. Tensor operations (addition, multiplication, etc.)

3. Data types and shapes

4. Computational graphs and eager execution

5. Automatic differentiation (GradientTape)

6. Introduction to NumPy vs TensorFlow

Lesson 3 - Data Handling & Preprocessing

1. Loading datasets (CSV, images, text)

2. Data pipelines with tf.data

3. Data preprocessing techniques

4. Feature scaling and normalization

5. Train-test split

6. Handling large datasets efficiently

Lesson 4 - Building Machine Learning Models

1. Introduction to model building in TensorFlow

2. Linear regression model

3. Loss functions and optimization

4. Gradient descent implementation

5. Model evaluation metrics

Lesson 5 - Neural Networks with TensorFlow & Keras

1. Introduction to Neural Networks

2. Perceptron and multilayer networks

3. Activation functions

4. Building models using Keras Sequential API

5. Model compilation and training

6. Avoiding overfitting (regularization, dropout)

Lesson 6 - Advanced Keras APIs

1. Functional API

2. Custom layers and models

3. Model subclassing

4. Callbacks (EarlyStopping, ModelCheckpoint)

5. Hyperparameter tuning

Lesson 7 - Computer Vision with TensorFlow

1. Introduction to image processing

2. Convolutional Neural Networks (CNN)

3. Image classification models

4. Transfer learning using pre-trained models

5. Data augmentation techniques

Lesson 8 - Natural Language Processing (NLP)

1. Text preprocessing and tokenization

2. Word embeddings

3. Recurrent Neural Networks (RNNs)

4. LSTM and GRU models

5. Text classification and sentiment analysis

Lesson 9 - Time Series & Sequential Data

1. Time series fundamentals

2. Sequence modeling

3. Forecasting with TensorFlow

4. RNN-based time series models

Lesson 10 - Model Optimization & Evaluation

1. Model performance metrics

2. Hyperparameter tuning

3. Handling bias and variance

4. Cross-validation

5. Model debugging techniques

Lesson 11 - TensorFlow Tools & Ecosystem

1. TensorBoard for visualization

2. TensorFlow Hub (pre-trained models)

3. TensorFlow Lite (mobile deployment)

4. TensorFlow Serving (production deployment)

Lesson 12 - Advanced Topics

1. Custom loss functions

2. Distributed training

3. Transfer learning and fine-tuning

4. Generative models basics

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TensorFlow Online Course Fees

Online Class Room Program

US $ 599.00

100% Money Back Guarantee

Duration : 40 Hrs
Plus Self Paced

Classes Starting From

Fast Track Batch 02 Jun 2026
Weekday Batch 08 Jun 2026
Weekend Batch 06 Jun 2026

Corporate Training

Customized Training Delivery Model
Flexible Training Schedule Options
Industry Experienced Trainers
24x7 Support

Trusted By Top Companies Worldwide

Want to know Today's Offer

TensorFlow Certification

igmGuru provides a recognized course completion certificate for TensorFlow. This certificate comes with lifetime validity and helps validate your skills in building AI applications using TensorFlow, including machine learning, deep learning, computer vision, and NLP projects. To earn the certification, simply enroll in the TensorFlow course and complete the required training and assessments.

TensorFlow Certification Course FAQ's

Is TensorFlow beginner friendly?

TensorFlow is known to have a steeper learning curve than many other machine learning tools. That said, there is nothing you cannot achieve with good training and excellent trainers.

What kind of jobs can I get after completing this TensorFlow training?

After completing TensorFlow training, you can pursue roles such as Machine Learning Engineer, Deep Learning Engineer, AI Developer, Data Scientist, Research Scientist, and AI Solutions Architect. These roles include building and deploying machine learning models, developing AI applications, analyzing large datasets, and implementing deep learning solutions to solve real-world problems. Expertise in TensorFlow is highly valued in industries like healthcare, finance, robotics, autonomous systems, and technology, where AI and machine learning drive innovation and business growth.

Is TensorFlow good for deep learning?

TensorFlow, developed by Google, is an open-source library that has essentially been developed for deep learning applications. Thus, igmGuru's TensorFlow online course helps you to increase the best career opportunities in the field.

Is TensorFlow better than PyTorch for building a career in AI?

Both are industry-standard frameworks. TensorFlow is mostly used in production environments and enterprise AI systems, and has a stronger presence in deployment roles (MLOps, mobile AI, edge AI). PyTorch is used in academic research. For a career focused on production AI and applied deep learning, and especially for Google Cloud or Android/iOS AI projects, TensorFlow remains a top choice. Many companies use both.