HARTMANN software group

Building Intelligent Applications with AI and ML - Level 2 Training in Plymouth

Enroll in or hire us to teach our Building Intelligent Applications with AI and ML - Level 2 class in Plymouth, Minnesota by calling us @303.377.6176. Like all HSG classes, Building Intelligent Applications with AI and ML - Level 2 may be offered either onsite or via instructor led virtual training. Consider looking at our public training schedule to see if it is scheduled: Public Training Classes
Provided there are enough attendees, Building Intelligent Applications with AI and ML - Level 2 may be taught at one of our local training facilities.
We offer private customized training for groups of 3 or more attendees.
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Course Description

  

Most intelligent applications involve using huge quantities of data in various formats. Deep learning and linguistics are widely becoming a part of intelligent applications in every field. Natural Language processing is one of the broadly applied areas of machine learning to effectively analyze massive quantities of unstructured, text-heavy data. Intelligent applications using NLP include models that analyze speech and language, uncover contextual patterns, and provide insights from text and audio.

This course focuses on covering deep learning concepts required to understand NLP and then focuses on introducing you to the concepts of NLP slowly taking you to basic and advanced NLP models for text processing, analytics, and building applications using NLP.

Course Length: 2 Days
Course Tuition: $1295 (US)

Prerequisites
Basic familiarity with Python programming. Basic understanding of Data Terminologies. Familiarity with enterprise IT. Foundational knowledge in mathematical concepts like linear algebra and probability Basic Linux skills Basic SQL skills Should have attended 'Building Intelligent Applications with Artificial Intelligence (AI) and Machine Learning (ML) Level 1

Course Outline

  
  1. Deep Learning Essentials
    • Understanding Neural Networks, Artificial Neural Network, Perceptron concepts
    • Understanding activation functions and why they are important?
    • Understanding Convolutional Neural Networks
    • CNN Architectures
    • CNN applications
    • Understanding Recurrent Neural Networks
    • RNN Architectures
    • RNN Applications
    • Natural Language Processing
      • Foundations of NLP
      • Various NLP Libraries
      • Understand NLP concepts like Morphology, Lemmetization, Stemming, Part-of-Speech tagging
      • Understanding Text Analytics
      • Performing Text Analytics with a case study
    • Natural Language Processing with Deep Learning
      • Applications of Deep Learning in NLP
      • Deep Learning Libraries for building NLP applications
      • Word Embeddings
      • Identifying Sentiments in Customer Reviews - case study
  2. Advanced NLP Models
    • Understand and Differentiate between LSTMs, GRUs, GPT Models
    • Understand Sequence to Sequence Models
    • Understand Attention Models
    • Understand Transformer Models
    • A Deep Dive into Machine Translation using NLP
    • Building a real-world End-End NLP Application
      • Data Gathering
      • Data Cleaning and Pre-processing
      • Building and evaluating NLP Models
    • GUI and REST APIs
      • Building UI for your Machine Learning Models
      • Building a REST API for your Models

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