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Getting Started with NLP and Deep Learning with Python [Video]

More Information
Learn
  • Explore the concept of Natural Processing Language and Recommendation Systems
  • Tokenize a sentence
  • Transform text tokens into numerical vectors by vectorizing
  • Modeling topics to identify common topics among documents
  • Find out about ANNs
  • Compute the gradients of all output tensors
  • Create a Machine Learning architecture from scratch
About

As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine Learning applications are everywhere, from self-driving cars to spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge.

In this course, you’ll be introduced to the Natural Processing Language and Recommendation Systems, which help you run multiple algorithms simultaneously. Also, you’ll learn about Deep learning and TensorFlow. Finally, you’ll see how to create an Ml architecture.

Style and Approach

An easy-to-follow, step-by-step guide that will help you get to grips with real-world applications of algorithms for Machine Learning.

Features
  • Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide
  • Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation
  • Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide
Course Length 1 hour 41 minutes
ISBN 9781789138894
Date Of Publication 26 Feb 2018

Authors

Giuseppe Bonaccorso

Giuseppe Bonaccorso is an experienced manager in the fields of AI, data science, and machine learning. He has been involved in solution design, management, and delivery in different business contexts. He got his M.Sc.Eng in electronics in 2005 from the University of Catania, Italy, and continued his studies at the University of Rome Tor Vergata, Italy, and the University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, bio-inspired adaptive systems, neuroscience, and natural language processing.