Expert Programmes

Deep Learning with Keras & Tensorflow – Master Certification Programme

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Course Description

Deep Learning also known as Deep Neural Learning, is a subset of machine learning, an application of AI, where machines imitate the workings of the human brain and employ artificial neural networks to process the information.

TensorFlow is an open source library created and released by google for numerical computation and building deep learning models.

In the traditional machine learning, most of the applied features need to be identified by a domain expert in order to reduce the complexity of the data. Whereas the biggest advantage of the Deep Learning algorithm is it tries to learn high-level features from data in an incremental manner, which makes the process simpler and popular. Deep Learning techniques outperform other techniques when the data size is large and complex, and also, this technique is behind many high-end innovations.

Deep learning is one of the newest technological advances in the fields of artificial intelligence and machine learning. This Deep Learning with Keras and TensorFlow course is designed to help you master deep learning techniques and enables you to build deep learning models using the Keras and TensorFlow frameworks. These frameworks are used in deep neural networks and machine learning research, which in turn contributes to the development and implementation of artificial neural networks.


Co-Developed by
IBM & SimpliLearn


Offered in Partnership with


Course Delivery
  • Online self-paced learning
  • Virtual classroom training

Total: 34 hours online blended learning


The Deep Learning market size currently surging at unprecedented growth rate. Industrial sectors like healthcare, information technology, fin-tech, and e-commerce need professionals with deep learning skills.


Skills to be Learned
  • Keras and TensorFlow Framework
  • PyTorch and its elements
  • Image Classification
  • Artificial Neural Networks
  • Autoencoders
  • Deep Neural Networks
  • Conventional Neural Networks
  • Recurrent Neural Networks
  • ADAM Adagrad and Momentum



Deep Learning with Keras and Tensorflow Certificate of Achievement from Simplilearn


Awarding Organisation



Learning Outcomes

When you complete this deep learning course, you will be able to accomplish the following:

  • Understand the concepts of Keras and TensorFlow, its main functions, operations, and the execution pipeline
  • Implement deep learning algorithms, understand neural networks, and traverse the layers of data abstraction
  • Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks, and high-level interfaces
  • Build deep learning models using Keras and TensorFlow frameworks and interpret the results
  • Understand the language and fundamental concepts of artificial neural networks, application of autoencoders, and Pytorch and its elements
  • Troubleshoot and improve deep learning models
  • Build your own deep learning project
  • Differentiate between machine learning, deep learning, and artificial intelligence



Quizz at end of lessons

Course End Projects


Certification Criteria
  • A score of at least 75% in course-end assessment
  • At least 85% attendance of one live virtual classroom
  • Successful evaluation in the course-end project


Who Should Enrol

Demand for skilled Deep Learning Engineers is booming across a wide range of industries, making this Deep Learning course with Keras and Tensorflow certification training well-suited for professionals at the intermediate to advanced level:

  • Software and IT professionals interested in analytics
  • Data scientists
  • Business/ data analysts who want to understand deep learning techniques
  • Statisticians with an interest in deep learning



It is recommended that you first complete the following courses in order to improve your ability to understand the deep learning course’s concepts:

  • Programming Fundamentals
  • Statistics Essentials
  • Concepts about Machine Learning


Course Overview
Lesson 01 – Course Introduction
Lesson 02 – AI and Deep learning introduction
Lesson 03 – Artificial Neural Network
Lesson 04 – Deep Neural Network & Tools
Lesson 05 – Deep Neural Net optimization, tuning, interpretability
Lesson 06 – Convolutional Neural Net
Lesson 07 – Recurrent Neural Networks
Lesson 08 – Autoencoders



Project 1 – PUBG Players Finishing Placement Prediction

Create a model that predicts players’ finishing placement based on their final stats, on a scale of 1 (first place) to 0 (last place).

Project 2 – Lending Club Loan Data Analysis

Create a model that predicts whether a loan will go into default using the historical data.


Accessible Period of Course

1 Year from date of enrolment


Customer Reviews


A. Anthony Davis

The Simplilearn Data Scientist Master’s Program is an awesome course! You learn how to solve real-world problems, and the wide variety of projects give you hands-on experience to make you industry-ready. The lecturers are experts and share their knowledge energetically. Thank you for an excellent learning experience.

Abhishek Tripathi

Good online content for data science. I completed Data Science with R and Python. The instructors have good knowledge on the subject. Self-learning videos help a lot, too. Thanks, Simplilearn.

Course Features

  • Students 0 student
  • Max Students1000
  • Duration34 hour
  • Skill levelall
  • LanguageEnglish
  • Re-take course1000


0 total

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