Level 3 Certificate in AI Programming with Python (RQF) TQUK

This subject is broken down into 80 topics in 8 modules:

  1. Introduction to Artificial Intelligence (AI) 10 topics
  2. Python Basics for AI 10 topics
  3. Data Science with Python 10 topics
  4. Machine Learning with Python 10 topics
  5. Natural Language Processing with Python 10 topics
  6. Python for Robotics 10 topics
  7. Deep Learning with Python 10 topics
  8. AI Project Development 10 topics
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  • 8
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  • 80
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  • 32,238
    words of revision content
  • 4+
    hours of audio lessons

This page was last modified on 28 September 2024.

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Certificate in AI Programming with Python (RQF)

Introduction to Artificial Intelligence (AI)

Definition and history of AI

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Definition and history of AI

Definition of Artificial Intelligence

  • Artificial Intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems.
  • These processes include learning (acquiring information and rules for using the information), reasoning (using rules to reach approximate or definitive conclusions), and self-correction.
  • Broadly, AI is classified into two types: narrow or weak AI, which is designed and trained for a particular task, and general or strong AI, with generalised human cognitive abilities.

Early History of AI

  • Thoughts about artificial beings were present in ancient mythologies, but the science of AI only conceptualized in the last century.
  • In 1950, Alan Turing proposed the notion that machines could be made to think, leading to what is now known as the Turing Test.
  • John McCarthy is recognised as the father of AI, after he coined the term 'artificial intelligence' in 1956, and then proposed the concept of machine learning.

Evolvement of AI over Time

  • The field of AI research was founded at a workshop on the Dartmouth College campus in the summer of 1956, which some consider the birth of AI.
  • During the 1960s and 1970s, AI research was focused on symbolic methods, also known as "rule-based" AI.
  • The 1980s marked the rise of machine learning with a focus on neural networks.
  • In the 1990s and early 21st century, AI began to be used for logistics, data mining, medical diagnosis and other areas.
  • The 21st century also marks the era of big data which provided more power to machine learning, producing dramatic advances in speech recognition, image recognition, and many other areas.

Current and Future Scope of AI

  • Currently, AI techniques are pervasive and are too numerous to name, but include machine learning, deep learning, neural networks, and natural language processing.
  • The future of AI promises a new era of disruption and productivity, where human ingenuity can be enhanced with this powerful technology.

Course material for Certificate in AI Programming with Python (RQF), module Introduction to Artificial Intelligence (AI), topic Definition and history of AI

Certificate in AI Programming with Python (RQF)

Natural Language Processing with Python

Introduction to Natural Language Processing (NLP)

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Introduction to Natural Language Processing (NLP)

General Introduction to Natural Language Processing (NLP)

  • Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that deals with the interaction between computers and humans through natural language.
  • The ultimate goal of NLP is to enable computers to understand, interpret and generate human text.
  • NLP combines the power of linguistic studies (grammar, semantics etc.) with computer science (algorithms, data structures, etc.) to produce machine-driven analyses of our language.

Components of Natural Language Processing

  • Syntax: It refers to the arrangement of words in a sentence in a way that makes sense in a certain language.
  • Semantics: It is concerned with the meaning of sentences.
  • Discourse: This refers to the way sentences connect to form a meaningful paragraph.
  • Speech: This deals with the vocalized form of human communication, in relation to NLP.

Applications of NLP

  • Machine translation is an application of NLP where the text in one language is translated to another language.
  • Sentiment analysis is another common application of NLP in which the sentiment or tone of a piece of writing is determined (positive, negative, neutral, etc.).
  • NLP is also used in Chatbots/AI assistants such as Siri, Google Assistant and Alexa to understand and generate responses to user inquiries.

Introduction to Python for Natural Language Processing

  • Python is often the preferred language for implementing NLP due to its simplicity and variety of libraries.
  • The Natural Language Toolkit (NLTK) library in Python provides tools for symbolic and statistical natural language processing.
  • Another library spacy is also widely used in the industry for various NLP tasks.

Steps involved in NLP using Python

  • Tokenization: It is the process of breaking down text into individual words or terms, known as tokens.
  • Stopwords removal: In this step, common words that do not contribute to the meaning of a sentence are filtered out.
  • Stemming and lemmatization: These are techniques to reduce inflectional forms and sometimes derivationally related forms of a word to a common base form.
  • Part of speech (POS) tagging: Each word is labeled as noun, verb, adjective, etc.
  • Named Entity Recognition (NER): Person names, locations, company names etc. are identified from the text.

Study the above points thoroughly to improve your understanding of Natural Language Processing and its implementation in Python. Ensure you understand the definitions of key terms - these often appear in test questions - and can explain how each Python library is used in different NLP tasks.

Course material for Certificate in AI Programming with Python (RQF), module Natural Language Processing with Python, topic Introduction to Natural Language Processing (NLP)

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