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Advanced Artificial Intelligence

irna Tue, 11/14/2023 - 07:51
AI2

Duration

3 Days

Exam Details

  • Exam Code: AI90.01: Artificial Intelligence Specialist
  • Duration: 110 Minutes
  • A passing grade on Exam AI90.01 is required to is required to achieve the Artificial Intelligence Specialist Certification.
  • Availability: Pearson VUE Testing Centers Worldwide, Pearson VUE Online Proctoring, On-Site Proctoring.

Price

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This course covers a series of practices for preparing and working with data for training and running contemporary AI systems and neural networks. It further provides techniques for designing and optimizing neural networks, including approaches for measuring and tuning neural network model performance. The practices and techniques are documented as design patterns that can be applied individually or in different combinations to address a range of common AI system problems and requirements. The patterns are further mapped to the learning approaches, functional areas and neural network types that were introduced in Module 1: Fundamental Artificial Intelligence.


Key Outcomes:
Students will be able to know:

  • Data Wrangling Patterns for Preparing Data for Neural Network Input
  • Text Representation for Converting Data while Preserving Semantic and Syntactic Properties
  • Image Identification for using a Convolutional Neural Network
  • Content Filtering for Generating Recommendations
  • Baseline Modeling for Assessing and Comparing Complex Neural Networks
  • Frequent Model Retraining for Keeping a Neural Network in Synch with Current Data
  • Transfer Learning for Accelerating Neural Network Training

Objectives

  • Data Wrangling Patterns for Preparing Data for Neural Network Input
  • Feature Encoding for Converting Categorical Features
  • Feature Imputation for Inferring Feature Values
  • Feature Scaling for Training Datasets with Broad Features
  • Text Representation for Converting Data while Preserving Semantic and Syntactic Properties
  • Dimensionality Reduction to Reduce Feature Space for Neural Network Input
  • Supervised Learning Patterns for Training Neural Network Models
  • Supervised Network Configuration for Establishing the Number of Neurons in Network Layers
  • Image Identification for using a Convolutional Neural Network
  • Sequence Identification for using a Long Short Term Memory Neural Network
  • Unsupervised Learning Patterns for Training Neural Network Models
  • Pattern Identification for Visually Identifying Patterns via a Self Organizing Map
  • Content Filtering for Generating Recommendations
  • Model Evaluation Patterns for Measuring Neural Network Performance
  • Training Performance Evaluation for Assessing Neural Network Performance
  • Prediction Performance Evaluation for Predicting Neural Network Performance in Production
  • Baseline Modeling for Assessing and Comparing Complex Neural Networks
  • Model Optimization Patterns for Refining and Adapting Neural Networks
  • Overfitting Avoidance for Tuning a Neural Network
  • Frequent Model Retraining for Keeping a Neural Network in Synch with Current Data
  • Transfer Learning for Accelerating Neural Network Training

Contact

  • Nurman (+62 857-2375-3840)
  • Irna (+62 822-1664-7749)
  • Puji (+62 813-2424-2115)