Overview of Course

Our TensorFlow Training course is designed to equip you with the skills to develop and deploy machine learning models using the TensorFlow framework. With hands-on training, you will gain practical experience in building, training, and deploying deep neural networks for various applications.

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Course Highlights

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In-depth coverage of TensorFlow and its applications

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Hands-on training with real-world datasets

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Expert-led training with industry-relevant use cases

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Skills You’ll Learn


Building and training neural networks using TensorFlow


Deep learning techniques for image and speech recognition


Natural language processing and text analysis


Time-series analysis and prediction


Deploying TensorFlow models for production use

Training Options

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1-on-1 Training

On Request
  • Option Item Access to live online classes
  • Option Item Flexible schedule including weekends
  • Option Item Hands-on exercises with virtual labs
  • Option Item Session recordings and learning courseware included
  • Option Item 24X7 learner support and assistance
  • Option Item Book a free demo before you commit!
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Corporate Training

On Request
  • Option Item Everything in 1-on-1 Training plus
  • Option Item Custom Curriculum
  • Option Item Extended access to virtual labs
  • Option Item Detailed reporting of every candidate
  • Option Item Projects and assessments
  • Option Item Consulting Support
  • Option Item Training aligned to business outcomes
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Course Reviews


  • Overview of deep learning and its applications
  • Historical background of deep learning
  • Types of deep learning algorithms
  • Advantages and limitations of deep learning
  • Comparison with traditional machine learning

  • Introduction to artificial neurons and neural networks
  • Activation functions and their role in neural networks
  • Backpropagation algorithm and its significance
  • Deep neural networks and their architecture
  • Regularization techniques to prevent overfitting

  • Introduction to deep neural networks
  • Deep feedforward networks and their architecture
  • Convolutional neural networks (CNNs) and their architecture
  • Recurrent neural networks (RNNs) and their architecture
  • Deep reinforcement learning and its significance

  • Overview of TensorFlow and its components
  • TensorFlow installation and setup
  • TensorFlow data flow graph and its significance
  • TensorFlow variables and constants
  • TensorFlow placeholders and their usage

  • Introduction to CNNs and their architecture
  • Convolutional layers and their role in CNNs
  • Pooling layers and their significance
  • Implementation of CNNs using TensorFlow
  • Applications of CNNs in computer vision

  • Introduction to RNNs and their architecture
  • Long short-term memory (LSTM) networks and their significance
  • Gated recurrent units (GRUs) and their role in RNNs
  • Implementation of RNNs using TensorFlow
  • Applications of RNNs in natural language processing

  • Introduction to RBMs and their architecture
  • Contrastive divergence algorithm and its significance
  • Introduction to autoencoders and their architecture
  • Denoising autoencoders and their significance
  • Applications of RBMs and autoencoders in unsupervised learning

  • Practice exercises to test the understanding of the concepts learned
  • Interview questions to prepare for deep learning-related job interviews
  • Discussion of common mistakes and best practices in deep learning
  • Tips for further learning and resources to explore
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Target Audience:

  • Data Scientists and Analysts
  • Machine Learning Engineers
  • Developers and Programmers
  • Students and Researchers
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  • Basic knowledge of Python programming
  • Understanding of linear algebra and calculus
  • Familiarity with machine learning concepts
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Benefits of the course:

  • Gain expertise in TensorFlow and deep learning techniques
  • Practical training with real-world datasets and use cases
  • Enhance your career prospects as a machine learning engineer or data scientist
  • Lifetime access to course materials and online support
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Exam details to pass the course:

  • There is no exam to pass the course
  • Participants will receive a certificate of completion upon finishing the course
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Certification path:

  • TensorFlow Developer Certification
  • TensorFlow Advanced Certification
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Career options:

  • Machine Learning Engineer
  • Data Scientist
  • Research Scientist
  • Software Developer

Why should you take this course from Skillzcafe:

Why should you take this course from Skillzcafe:
  • Bullet Icon Industry-relevant curriculum and training materials
  • Bullet Icon Expert-led training with real-world use cases
  • Bullet Icon Lifetime access to course materials and online support
  • Bullet Icon Flexible learning options with self-paced and instructor-led modes


Yes, this course is suitable for beginners as well as experienced professionals.

The course duration is approximately 50 hours.

Please check our website for current pricing information.

Yes, you will need to install TensorFlow on your local machine to complete the course exercises.

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