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Practical Artificial Intelligence (AI) - Level 4 Course

Course Format:Online Course
Self-Study Time:Approximately 140 Hours (Self-Study)
Delivery Time:1-2 Working Days (Email)
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Discover the potential of Artificial Intelligence (AI) using Python

Our Practical Artificial Intelligence - Level 4 Course takes you behind the scenes of this developing technology, providing everything you need to know, expressed in simple terms, to gain an understanding of the complex technologies behind AI, and including numerous hands-on practical exercises and projects using the Python programming language (all of the code is provided, and we even include a crash course in Python for those who need it).

Artificial intelligence (AI) is a set of technologies, including machine learning, deep learning, and neural networks, that enable computers to perform human-like advanced functions, including the ability to see, understand and translate spoken and written language, analyse data, make recommendations, and much more, including controlling self-driving cars.

We investigate how computers are able to use historical data to learn how to make predictions, like forecasting a stock price movement or a cricket match score, how to recognise images, such as faces and traffic signs, and we dive under the hood to examine the technologies behind self-driving cars. We also explore the current state-of-the-art in robotics, including the use of AI-powered robotic arms in surgery and a range of manufacturing industries and delve into the techniques behind Natural Language Processing and automatic language translation.

Each unit is designed to build on what you learned in the previous unit and the course is designed around the “learn-by-example” methodology, with a wide range of real-life examples provided to demonstrate new concepts as you learn them. You also get the chance to practice your skills with a variety of interesting exercises and assignments. You will even build your own neural network using advanced deep learning techniques!

On successful completion of the course, students will receive the Distance Learning Centre Practical Artificial Intelligence Diploma with feedback and analysis of your completed work, as well as an Open Awards Quality Endorsed Unit Course Certificate.

Course Format

The lesson notes are delivered through slides giving you the required information to work through each lesson and there are student files you will download to work on so you are putting into practice what you are learning. You will also be supported by a personal tutor who is available to provide feedback on your work at any time throughout the period of study.

Based on the proven "learn by example” principle which, as the name implies, provides real-world examples to illustrate a particular topic or technique, the courses are delivered through our on-line training portal via a standard web browser, such as Chrome, Firefox, Edge, Opera, or Safari.

The Practical AI Course Includes the Following Units:

This unit will help you to understand what AI needs to work successfully, together with the underlying technology, which we will find at work in several areas, including medicine, human interaction and aeronautics, to mention just a few. AI is also closely entwined with data analysis, machine learning, deep learning and neural networks.

The following topics are covered:

  • Introduction
  • An overview of AI
  • The underlying technologies
  • The role of data
  • The use of algorithms
  • The development of specialised hardware
  • Unit review and quiz
  • Assignment 1 – Data analysis exercise

As we know, machine learning (ML) relies on algorithms to analyse huge datasets and, although ML can’t think or feel like a human, it can perform predictive analytics far faster than a human and can help humans work more efficiently.

This unit will introduce us to the tools we need to perform machine learning and will provide us with a solid understanding of how machine learning works.

The following topics are covered:

  • Introduction
  • Preparing your machine learning tools
  • Python basics (1)
  • Python basics (2)
  • Understanding the maths
  • How machine learning works
  • How machine learning works with data
  • Examples of learning from data
  • Unit review and quiz
  • Assignment 2 – Iris machine learning project

In this unit, we will learn how to obtain meaningful data, acquire enough data for the learner algorithm to work correctly, arrange the data into a matrix, deal with bad data, such as missing cases, distorted distributions, and anomalous examples, and create new features that are better suited to our algorithm.

We will also discover how to understand data through similarity, work with some linear models and neural networks, deploy smart vector machines, and perform multiple levels of analysis using ensembles.

The following topics are covered:

  • Introduction
  • Preprocessing data
  • Leveraging similarity
  • Working with linear models
  • The complexity of neural networks
  • Support Vector Machines (SVM)
  • Ensembles of learners
  • Unit review and quiz
  • Assignment 3 – Data cleaning project

In recent years, machine learning has often been used to classify images and read text for a variety of reasons and we start this unit by exploring techniques for obtaining image features for use in machine learning models, before moving on to investigate natural language processing.

We also investigate a number of machine learning products and explore ways to improve machine learning models and study some guidelines for using data ethically.

The following topics are covered:

  • Introduction
  • Classifying images
  • Working with text
  • Recommender systems
  • Ways to improve machine learning models
  • Guidelines for ethical data use
  • Machine learning packages
  • Unit review and quiz
  • Assignment 4 – Image recognition project

Today, we are surrounded by deep learning without even realising it. It is on our smartphones and every time we shop online and, in this unit, we aim to uncover the mystery surrounding deep learning, including its benefits and its limitations.

As with previous units, we provide the code for our deep learning examples and you should prepare to be amazed at some of the techniques we will explore.

The following topics are covered:

  • Introduction
  • Introducing deep learning
  • Using a deep learning framework
  • Deep learning fundamentals
  • Moving from machine learning to deep learning
  • More on Convolutional Neural Networks (CNN)
  • Recurrent neural networks (RNN)
  • Unit review and quiz
  • Assignment 5 – Stock price prediction project

All of us will have been exposed to some form of AI during our daily lives. If you shop on Amazon, you will be presented with items that other customers have bought together with your item (based on buying patterns generated by AI). If you talk to your smartphone and it understands what you want, that is speech recognition using AI at work.

The following topics are covered:

  • Introduction
  • AI in computer applications
  • Automating common processes
  • Using AI for medical purposes
  • Improving human interaction
  • Robotics
  • Self-driving cars
  • Unit review and quiz
  • Assignment 6 – IPL cricket score predictor project
Prerequisites

We recommend that learners have a sound general knowledge of IT and a good working knowledge of the Python programming language, but this is not a mandatory requirement.

The hardware and software requirements include:

  • Intel or equivalent processor (multiple CPUs would be advantageous, but not required);
  • A graphics card (GPU) would be advantageous, but not required;
  • 2mb RAM;
  • 500mb available disk storage;
  • An Internet connection;
  • Windows, MacOS or Linux operating system;
  • Google Chrome browser.
Course Duration & Online Support

You can start this course at any time, and you would be registered for a one-year period. You also have access to a personal tutor who can be contacted by email. Your tutor is available to assist with any queries and to mark your assignments for the Awarding Body.

As the course is self-study, you can complete it as quickly as you like. Support extensions are available if students do not complete the course within the one-year period.

Assessment

All of the assignments are projects or experiments that the learners are guided through. You will produce screenshots of the end-result to confirm completion of the project and these can be emailed to your course tutor by on completion of your training.

Open Awards Certification

Open Awards

On successful completion of this course students will receive our Practical Artificial Intelligence Diploma with feedback on your work and providing the assignments have been completed to the required standards students will also receive a Level 4 Open Awards Quality Endorsed Unit Course Certificate with 9 Open Awards Credits.

The completion of this course alone does not lead to an Ofqual regulated qualification but may be used as evidence of knowledge and skills towards regulated qualifications in the future. To this end the learning outcomes of the course have been benchmarked at Level 4 against level descriptors published by Ofqual, to indicate the depth of study and level of difficulty involved in successful completion by the learner.

You can find further information on qualifications/certificates and their levels on the Ofqual's level descriptors page.

The certification is issued through Open Awards. Open Awards are an Awarding Body Organisation approved by Ofqual. Set up in 1981, Open Awards (Previously the North West Region of the National Open College Network - OCNNW) have been in business for over 30 years and are a not for profit organisation and a registered charity.

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Practical Artificial Intelligence (AI) - Level 4 Course
Practical Artificial Intelligence (AI) - Level 4 Course
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