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Deep Learning

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4.6 (1273 ratings)
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lifelong access

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Beginner level

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1842+ learners

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Language: English

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8 Hours

Break into Deep Learning by learning through projects. Revise the Machine Learning concepts and learn the basics of Deep Learning.

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Dr. Deepali Vora

Deep Learning
8 Hours

Chapters

Course Overview

Course Overview

This course will enable you to

  • List Item Revise the concepts of ML and DL and introduces to the tools of Deep Learning
  • List Item Apply the Neural Networks by understanding the structure and training using Backpropagation
  • List Item Learn Advanced Deep Learning algorithms like CNN, RNN and LSTM
  • List Item Learn generative algorithms like Deep Belief Network and Deep Autoencoders
  • List Item In depth understanding and programming of various Deep learning algorithms

What’s included?

  • List Item2 project files
  • List ItemLearning groups
  • List ItemCertificate on completion
  • List ItemInternship opportunities
  • List ItemFlexible learning & lifelong access

Learner’s from this course got opportunities at

+142 more

Careers ahead

Lead Data Scientist, Machine Learning Scientist, AI Engineer

More details

This century is experiencing growth with data. Data has gained importance equivalent to fuel. The data drives the businesses and so data-driven techniques have gained importance. The machine learning techniques and in turn Deep Learning has become a primary focus for driving the business as they are meant to handle tremendous data. Deep Learning, which is a part of machine learning, consists of algorithms and techniques inspired by the human brain to handle large data. According to the survey, Deep Learning is the most demanding field which requires researchers as well as Data Analysts. If you are looking for a career in Deep Learning then this course is most suitable. The course will make you understand every smaller concept related to deep learning. The course starts with basic building blocks of deep learning, applications, and necessity. Identification of the right tool for implementation is the most challenging part and the course will touch upon many tools and choose Tensorflow as the ideal library for demonstration. The course starts the foundation of Deep Learning by making you understand the Neural Network architecture. It then proceeds further with CNN, RNN, LSTM as the variants of deep architecture. The autoencoders and Deep belief network strengthen the knowledge further. Deep Learning has wide application in various industries like healthcare, security, CRM, trading, insurance, etc. If you are looking for opportunities in these areas, then the course is the right choice to start pursuing a dream career. Put your career on the fast track by mastering the skills of Deep learning.

Learn the unschool way

Learn the unschool way

Experiential learning
Experiential learning

Internships, projects and practical teaching methods. Unschooling all the ways of traditional textbook knowledge.

Experiential learning
Weekly discussions and webinars

Get industry insights and updates by subject matter experts every week.

Experiential learning
Community

Become a part of an inspired community within and beyond your field of interest. Get access to unlimited opportunities.

About the instructor

About the instructor

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Dr. Deepali Vora

Ph.D. in Computer Science & Engineering

Dr. Deepali Vora is a BE, ME, Ph.D. in Computer Science & Engineering with specialization in Machine Learning and Deep Learning. She has 20+ years of combined experience working in industry and academia and is extensively working on many projects in the domain of Machine Learning. Her research is published in many reputed international journals and conferences.

  • Courses1 course
  • Students1842 students
  • Ratings4.6 instructor ratings

Student reviews

Student reviews

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Aayushi
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It was a really interesting course. Every mentor was friendly and cooperative all the time in clearing doubts. Explained every concept clearly which made me easier to understand all the topics up to the mark.

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Frequently asked questions

Frequently asked questions