Learning Paths

Deep Learning Literacy - Practical Application

  • Number of Courses12 courses
  • Duration15 hours
  • Expanded

Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised.

This path is focused on Deep Learning in action. We have pulled a series of examples to demonstrate how deep learning is embedded in our day to day lives. These are just in time sort of courses that reflect the journey from problem to solution.

The path is curated for Data enthusiasts that are eager to learn about Deep learning and foray into Data centered roles like Data scientist. Though this path will contain workable solutions, there is no requirement for the learner to have any background into Machine Learning or Deep learning. Intention is to have sandboxes for the path.

Courses in this path

Beginner

This path is designed to explore the application of Deep Learning in our day to day lives. This section focuses on a few industry examples of Deep Learning Practical applications.

Deep Learning Application for Healthcare

  • by Colin Matthews
  • 1h 2m Duration

Deep Learning Application for Marketing

  • by Netta Tzin
  • 52m Duration

Deep Learning Application for Finance

  • by Pluralsight
  • 34m Duration
  •  (31)

Deep Learning Application for Retail

  • by Trent McMillan
  • 1h 28s Duration

Intermediate

This section of the path focuses on RNN, CNN and GAN’s implementation and exploring how deep learning solves problems like automating image captioning and sentiment classification.

Implement Image Recognition with a Convolutional Neural Network

  • by Pratheerth Padman
  • 1h 30m Duration

Implement Text Auto Completion with LSTM

  • by Biswanath Halder
  • 1h 34m Duration

Implement Image Captioning with Recurrent Neural Networks

  • by Abdul Rehman Yousaf
  • 1h 33m Duration

Sentiment Classification with Recurrent Neural Networks

  • by Biswanath Halder
  • 1h 32m Duration

Advanced

This section explores practical examples of word embedding, Sentence classification, Named Entity Recognition and Recommendation Engines.

Implement Natural Language Processing for Word Embedding

  • by Axel Sirota
  • 1h 33m Duration

Build a Rating Recommendation Engine with Collaborative Filtering

  • by Pratheerth Padman
  • 1h 20m Duration

Build an Object Detection Model with Python

  • by Pluralsight
  • 1h 12m Duration

Build a Model for Anomaly Detection in Time Series Data

  • by Pratheerth Padman
  • 1h 13m Duration

Learn with the best

Colin Matthews
Colin Matthews
Colin is an experienced technical professional specializing in healthcare technology. Whether it's building full-stack web apps for side-projects, creating content online, or managing developers in his day job as a Product Manager, Colin always shows up with a smile and a willingness to learn.
Netta Tzin
Netta Tzin
Netta is a Product and Growth expert, who’s been utilizing her talents as a Product Manager in insure-tech and music-tech fields, as well as a Product consultant for various organizations. Her background combines both academic degrees in Industrial Engineering (Bsc.) and in International Management (Msc.), along with work experience of over 10 years in strategizing, structuring, and executing projects and products, both in startups and corporations. In the past few years, Netta has focused on le... more
Trent McMillan
Trent McMillan
Trent has a master's degree in Information Systems from Brigham Young University and a love for all things data. When he isn't on his computer he enjoys dirt bikes, DIY projects, and spending time with family and friends.
Pratheerth Padman
Pratheerth Padman
Pratheerth is a Data Scientist who has entered the field after an eclectic mix of educational and work experiences. He has a Bachelor's in Engineering in Mechatronics from India, Masters in Engineering Management from Australia and then a couple of years of work experience as a Production Engineer in the Middle East. Then when the A.I bug bit him, he dropped everything to dedicate his life to the field. He is currently working on mentoring, course creation and freelancing as a Data Scientist.
Biswanath Halder
Biswanath Halder
Biswanath is a Data Scientist who has around nine years of working experience in companies like Oracle, Microsoft, and Adobe. He has extensive knowledge of Machine Learning, Deep Learning, and Reinforcement Learning. He specializes in applying Machine Learning and Deep Learning techniques in complex business applications related to computer vision and natural language processing. He is also a freelance educator and teaches Statistics, Mathematics, and Machine Learning. He holds a Master's degre... more
Abdul Rehman Yousaf
Abdul Rehman Yousaf
Abdul Rehman is the founder of Pythonist.org, a software consulting, training, and application development company. Currently, he is working as a Senior Machine Learning Engineer at Nexthon Technologies where he built several amazing projects powered with artificial intelligence. Prior to that, he worked as a Cloud Solution Architect, building powerful, secure, and scalable infrastructures on various cloud vendors like Google Cloud Platform and AWS. During both of these roles, he used Python as ... more
Axel Sirota
Axel Sirota
Axel Sirota is a Microsoft Certified Trainer with a deep interest in Deep Learning and Machine Learning Operations. He has a Masters degree in Mathematics and after researching in Probability, Statistics and Machine Learning optimisation, he works as an AI and Cloud Consultant as well as being an Author and Instructor at Pluralsight, Develop Intelligence, and O'Reilly Media.

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