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AI Fundamentals

This one day workshop will introduce to the terminology, tools and high level considerations that need to be considered and understood to ensure the best possible outcome for an AI implementation.

Target Audience

The workshop is aimed at the individuals charged with implementing an AI solution.

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Learning Objectives

At the end of this workshop delegates will:

  • Gain a greater understanding of the tools used in AI
  • Be able to identify the hardware required to optimise an AI solution
  • Have a greater insight into Data Analysis and Analytics within AI
  • Be equipped to realise the benefits of Machine Learning and Deep learning

Pre-Requisites

A desire to understand where AI can be beneficial to your business.

Course Content

AI Tools

Data Science Languages

The Role of Python in Artificial Intelligence

Python Libraries for Artificial Intelligence

  • NumPy: NumPy the computing library for Python
  • SciPy: SciPy is an advanced library containing algorithms that are used for data science
  • scikit-learn: scikit-learn is Python's main machine learning library
  • NLTK: Library for natural language processing
  • TensorFlow: TensorFlow is Google's neural network library used for implementing deep learning artificial intelligence

Understanding the Role of Algorithms

  • Planning and branching
  • Local search and heuristics

Using expert systems

Hardware

  • Standard Hardware
  • Von Neumann bottleneck
  • Single points of failure
  • Tasking and multitasking

Specialised Hardware

  • Graphic Processing Units (GPUs)
  • Why are GPU’s suited to this field?
  • Application Specific Integrated Circuits (ASICs)
  • Field Programmable Gate Arrays (FPGAs)
  • Specialized Sensors

Data Powers AI

  • What is Data Science?
  • Big Data
  • Data Structures and Formats
  • Data Sources
  • Data Storage

Data Quality and Readiness

  • Data quality and readiness is key to a successful implementation. intelligence is based on knowledge and data is the raw material
  • Balance
  • Representative
  • Completeness
  • Clean Data

Predictive Analytics

  • Regression
  • Classification

Data Analysis for AI

  • Transforming: Changes the data’s appearance
  • Cleansing: Fixes imperfect data
  • Inspecting: Validates the data
  • Modelling: Discovers the relationship between the elements present in data

Define Machine Learning

How machine learning works

What are the benefits of machine learning?

  • Automation
  • Fraud detection
  • Customer service
  • Resource scheduling
  • Resource scheduling
  • Safety systems
  • Machine efficiency

Learning Models

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning

Machine learning approaches

  • Nai¨ve Bayes
  • Bayesian networks graph
  • Decision trees

Enhancing AI with Deep Learning

  • Simple neural networks
  • The strength of the connection between neurons in the network
  • Continuous learning using online learning
  • Reusable solutions using transfer learning
  • End-to-end learning

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Virtual Classroom

Virtual classrooms provide all the benefits of attending a classroom course without the need to arrange travel and accomodation. Please note that virtual courses are attended in real-time, commencing on a specified date.

Virtual Course Dates

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