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Artificial Intelligence & Machine Learning

Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Introduction to Artificial Intelligence Course Outline

Module 1: What is Artificial Intelligence (AI)?

  •   Introduction to Artificial Intelligence
  •   AI Approaches

Module 2: Application Areas of AI

  • AI in Healthcare
  • AI in Education
  • AI in Business
  • AI in Finance
  • AI in Law
  • AI in Manufacturing
  • Parents Disciplines of AI

Module 3: Artificial Intelligence and Related Fields

  • Logical AI
  • Search
  • Pattern Recognition
  • Knowledge Representation
  • Planning
  • Epistemology
  • Ontology

Module 4: Foundation of AI – Machine Learning

  • Foundation of Artificial Intelligence
  • Machine Learning
  • Strengths and Limitations of Machine Learning-Based AI
  • Machine Learning Methods
  • Supervised Machine Learning Algorithms
  • Unsupervised Machine Learning Algorithms
  • Semi-Supervised Machine Learning Algorithms
  • Reinforcement Machine Learning Algorithms

Module 5: Agents and Environments

  • Agents
  • Agent Terminology
  • Structure of Intelligent Agents
  • Types of Agents
  • Nature of Environments
  • Properties of Environment

Module 6: Concept of Rationality

  • Rationality
  • Rational Agents
  • Perfect Rationality

Module 7: Fuzzy Logic Systems

  • About Fuzzy Logic
  • Purpose of Fuzzy Logic
  • Fuzzy Logic Systems Architecture (FLS)
  • Application Areas of Fuzzy Logic
  • Fuzzy Logic Systems Advantages

Module 8: Overview of Robotics

  • Machine Learning in ANNs
  • Aspects of Robotics
  • Robot Locomotion
  • Components of a Robot
  • Applications of Robotics

Module 9: Natural Language Processing

  • Introduction to NLP
  • Components of NLP
  • NLP Terminology
  • Steps in Natural Language Processing

Module 10: Neural Networks

  • Artificial Neural Networks
  • What is a Neural Network?
  • Types of Artificial Neural Networks
  • Working of Artificial Neural Networks

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Prerequisites

In this Introduction to Artificial Intelligence Training course, there are no formal prerequisites.

Audience

This Introduction to Artificial Intelligence Training course is suitable for anyone who is willing to get in-depth knowledge of Artificial Intelligence. However, this course will be more beneficial for Technical and Non-Technical Professionals out there.

Introduction to Artificial Intelligence Course Overview

Artificial Intelligence (AI) is a group of methodologies in which the core algorithms are implemented in power structures such as fuzzy logic systems, artificial neural networks, colony optimisation, genetic algorithm, particle swarm optimisation, simulated annealing, and evolutionary computing. It is accomplished by analysing how the human brain functions while solving problems and using these outcomes to develop intelligent software and systems. AI is a field that is actively and continuously growing and changing. Attending this training course will help individuals to enhance the skills required to become successful AI professionals. This course will allow individuals to master AI applications, machine learning, natural language processing needed to excel in this domain and kick-start their career in Artificial Intelligence.

In this 1-day Introduction to Artificial Intelligence Training course, delegates will get to know about various functions, features, and uses of Artificial Intelligence. They will learn about different concepts such as machine learning in ANN's, artificial neural networks, Natural Language Processing (NLP), types of agents, agent's terminology, and more. They will understand the crucial role of Artificial Intelligence in various fields like healthcare, business, education, finance, law, and manufacturing. Delegates will also learn about the strength and limitations of machine learning-based AI, machine learning methods, and supervised, unsupervised, and semi-supervised machine learning algorithms, which will help them enhance their skills of working with AI.

This course includes various essential concepts as following:

  • What is Artificial Intelligence (AI)?
  • Application areas of AI
  • Artificial Intelligence and related fields
  • Foundation of AI – machine learning
  • Concept of rationality
  • Fuzzy logic systems
  • Overview of robotics
  • Natural Language Processing (NLP)

At the end of this Introduction to Artificial Intelligence Training course, delegates will be able to able to work with fuzzy logic systems and machine learning tools. They can automate grading in the education sector with Artificial Intelligence's help and provide additional support to students with AI tutors. They may also be able to use AI automation within business sectors for repeatable tasks that humans usually handle.

After completing this Introduction to Artificial Intelligence training course, delegates can choose from The Knowledge Academy's wide range of courses from Artificial Intelligence & Machine Learning topic for enhancing their skills in the advancing world of AI.

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What's included in this Artificial Intelligence Training Course?

  • The Knowledge Academy's Artificial Intelligence Course Manual
  • Experienced Instructor
  • Certificate
  • Refreshments

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Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Machine Learning Course Outline

  • Machine learning - Introduction
  • Importance of Machine Learning and its Techniques
  • Data Pre-processing
  • Machine Learning Mathematics
  • Supervised Learning
  • Classification
    • Support Vector Machines
    • Discriminant Analysis
    • Naive Bayes
    • Nearest Neighbour
  • Regression
    • Linear Regression and GLM
    • SVR and GPR
    • Decision Tree
  • Neural Networks
  • Unsupervised Learning
  • Clustering
    • K-Means, K-Medoids, Fuzzy and C-Means
    • Hierarchical
    • Gaussian Mixture
    • Hidden Markov Model
  • Deep Learning - Introduction

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Who should attend this Machine Learning Training Course?

This training course is beneficial for the delegates who wish to get proficient in machine learning. Professionals who are best suited for this course:

  • Graduates who wish to build a career in machine learning
  • Analytics managers
  • Information architects who wish to gain proficiency in machine learning algorithms
  • Experienced Professionals who would like to gain more insights in their fields
  • Developers intending to become a machine learning engineer or data scientist

Prerequisites

It is recommended that the delegates should possess some basic knowledge of python programming, statistics fundamentals, and high school mathematics for this course.

 

Machine Learning Course Overview

Machine learning is an application of artificial intelligence (AI). It is typically concerned with the computer programs that enable computers to evolve behaviours based on empirical data. Today, Machine Learning becomes a key technique to solve various problems in multiple areas such as computational finance and biology, NLP, and energy production.

This Machine learning training course will assist the delegate to gain mastery over machine learning. This training course guides the delegates through the different concepts of machine learning such as neural networks, algorithms, clustering, supervised and unsupervised learning. By the completion of the training course, the delegate will gain expertise in creating algorithms and applications in machine learning.

Machine Learning is one of the leading career choices these days. The demand for machine learning is increasing day by day, so in the upcoming years, the companies are expected to increase their investments in this technology.

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What's included in this Machine Learning Training Course?

  • The Knowledge Academy’s Machine Learning Training Course Manual
  • Experienced Instructor
  • Completion Certificate
  • Refreshments

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Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Deep Learning Course Outline

Machine Learning Basics

Introduction to Deep Learning

  • Importance of Deep Learning
  • How Deep Learning Works
  • Difference between Deep Learning and Machine Learning

Artificial Neural Networks

Deep Neural Networks

  • Feedforward Networks
  • Convolutional Networks
  • Recurrent and Recursive Networks

Linear Algebra

Probability

  • Random Variables
  • Probability Distributions
  • Marginal Probability
  • Conditional Probability
  • Chain Rule of Conditional Probabilities
  • Bayes’ Rule

Autoencoders

Computational Graphs

Monte Carlo Methods

Deep Generative Model

  • Boltzmann Machines

Applications

Libraries and Frameworks

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Prerequisites

It is recommended that delegates possess some basic knowledge of Python, linear algebra and probability.

Who should attend this Deep Learning Training?

This course is intended for individuals who wish to learn how deep learning works and who may wish to develop their own applications using deep learning techniques. 

Deep Learning Course Overview

Deep Learning is used for building and training neural networks – layers of decision-making nodes inspired by the human brain.

It is a subset of machine learning (ML) where artificial neural networks learn from large amounts of data. It allows machines to solve complicated problems even when using diverse, unstructured, and interconnected data sets.

This Deep Learning Training course will provide you with a basic understanding of the linear algebra, probabilities, and algorithms used in deep neural networks. After learning the difference between deep learning and machine learning, delegates will gain in-depth knowledge of the different types of neural networks such as feedforward, convolutional, and recursive. At the end of this course, delegates will be able to build complex models that help machines to solve real-world problems.

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What's included in this Deep Learning Course?

  • The Knowledge Academy’s Deep Learning Training Manual
  • Experienced Instructor
  • Completion Certificate

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Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Deep Learning with TensorFlow Course Outline

  • Introduction to TensorFlow
    • Tensors
    • Two Computation Phrases
    • A Computational Graph with TensorBoard
    • Variables
    • Linear Regression
    • Operations
  • Installation of TensorFlow
  • Artificial Neural Network
  • Activate Functions
  • Deep Learning Techniques
    • Convolutional Neural Networks
    • Recurrent Neural Networks
  • Deep Learning Applications
  • Computing Gradients
  • Single-layer and Multi-layer Perceptron
  • Back Propagation with TensorFlow

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Who should attend this Deep Learning Training Course?

This training course is designed for engineers who wish to get expertise in using the TensorFlow. It is recommended for the following professionals:

  • Data Scientists
  • Software Engineers
  • Data Analysts

Prerequisites

Basic knowledge about Python programming and machine learning can help the delegate to learn this course easily. 

Deep Learning with TensorFlow Course Overview

TensorFlow is an open-source software library of Google for implementing the Deep Learning – Artificial Neural Network. It works through layers of nodes to determine the correct outcome. This deep learning with TensorFlow training course will provide the delegate with skills in deep learning techniques using TensorFlow.

The participants will learn the use of Google’s library TensorFlow to solve the various real-world problems. By the completion of this course, the delegate will be able to implement algorithms, build and manage artificial neural networks.

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What's included in this Deep Learning Training Course?

  • The Knowledge Academy’s Deep Learning with TensorFlow Training Course Manual
  • Experienced Instructor
  • Completion Certificate
  • Refreshments

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Online Instructor-led (2 days)

Classroom (2 days)

Online Self-paced (16 hours)

Natural Language Processing (NLP) Fundamentals with Python Course Outline

Module 1: Introduction to NLP

  • What is Natural Language Processing?
  • Why is NLP Important?
  • Applications of NLP
  • Challenges in NLP
  • Tools and Resources for NLP

Module 2: Text Preprocessing

  • Text Cleaning and Normalisation
  • Tokenisation
  • Part of Speech Tagging
  • Named Entity Recognition
  • Stop Word Removal

Module 3: Text Representation

  • Bag of Words
  • Term Frequency-Inverse Document Frequency (TF-IDF)
  • Word Embeddings
  • Topic Modeling

Module 4: Text Classification

  • Supervised Learning
  • Naive Bayes
  • Support Vector Machines (SVM)
  • Decision Trees
  • Evaluation Metrics for Text Classification

Module 5: Advanced NLP Techniques

  • Sequence Labeling
  • Language Modeling
  • Neural Machine Translation
  • Sentiment Analysis
  • Text Summarisation

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Who should attend this NLP course?

Prerequisites

Before attending this Natural Language Processing (NLP) Fundamentals with Python Training course, delegates should have a basic knowledge of Python.

Audience

This training course is suitable for everyone who wishes to gain knowledge regarding Natural Language Processing. However, this course is more beneficial for:

  • Data Scientists
  • Developers
  • Python Professionals
  • Programmers
  • Data Analysts

Natural Language Processing (NLP) Fundamentals with Python Course Overview

Natural Language Processing (NLP) is the field of artificial intelligence that enables computers to comprehend spoken and written language like humans. It is an enterprise solution that improves employee productivity, simplifies mission-critical business processes, and streamlines business operations. This training aims to provide individuals with knowledge on how to deconstruct human text and voice data to help the computer understand and absorb the text and voice data. This training will equip learners with the algorithms and methods to derive meaningful information from raw data and enable the computer to understand and process human language. Individuals with knowledge of NLP and technical expertise will be able to advance their career opportunities and claim higher pay. 

In this 2-day Natural Language Processing (NLP) Fundamentals with Python Training course, delegates will gain comprehensive knowledge of natural language processing and how to use it effectively. While attending this training, they will learn to use the Natural Language Toolkit (NLTK) to pre-process raw text and use NLTK with different Python libraries. They will also learn about text classification, which involves classifying text strings or documents into different categories depending on the string's content. Our highly skilled tutor will conduct this course and help delegates practice with the NLP toolkit and various algorithms.

Course Objectives

  • To serve the interaction between computers and humans
  • To derive meaning from human languages using machine learning
  • To transfer linear sequences of words into structures with semantic analysis
  • To analyse language-based data compared to humans without bias
  • To analyse large paragraphs of text by splitting them into sentences
  • To find the lemma and root form of the word through lemmatisation

After attending this training course, delegates will be able to auto-summarise the text by using machine learning and developing natural language processing software. They will also be able to identify patterns and relationships within the huge amount of text.

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What's included in this NLP training course?

  • Delegate pack consisting of course notes and exercises
  • Experienced Instructor

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Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Artificial Intelligence (AI) for Project Managers Course Outline

Introduction to Artificial Intelligence (AI)

  • Aim and Purpose
  • Scope of Research
  • AI within the Corporate Context

Theoretical Framework

  • Industry 4.0
  • Types of AI Systems
  • Project Management Fundamentals
  • Future of Project Management
  • AI for Project Management
  • SWOT Analysis

Methodology

  • Research Strategy
  • Research Design
  • Research Process
  • Research Quality – Reliability, Replicability, and Validity
  • Ethical Considerations

Results – Surveys

  • Artificial Intelligence
  • Project Management
  • Organisational Business

Results – Interviews

  • Project Management
  • Artificial Intelligence

Analysis and Discussion

  • Project Management Community Requirements
  • Awareness of AI Systems
  • Building AI Systems for Project Managers
  • Implementing AI for Project Managers in the Organisation

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Prerequisites

There are no prerequisites for this course, anyone with an interest in AI or the way that AI can support project management should attend.

Audience

This course is designed for Project Managers with an interest in developing their awareness of AI and how AI can support their projects, operations, and efficient management

Artificial Intelligence (AI) for Project Managers Course Overview

Artificial Intelligence (AI) is a collection of techniques inspired by the goal of understanding and executing intelligent behaviour. By attending this Artificial Intelligence (AI) for Project Managers course, delegates will gain an insight into project management fundamentals, SWOT analysis, methodologies, etc. By attending this course, delegates will gain extensive knowledge of how Artificial Intelligence (AI) can be used within the corporate context. This training course will help project managers to add values in separate phases of the project lifecycle. On completion of this course, delegates will learn to build AI Systems and will also learn how to implement AI in their organisation.

 

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  • Delegate pack consisting of course notes and exercises
  • Manual
  • Experienced Instructor

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Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Artificial Intelligence (AI) for Business Analysts Course Outline

Introduction to Artificial Intelligence (AI)

Use of Artificial Intelligence (AI)

  • In Banking
  • In Finance
  • In Investment

Artificial Intelligence (AI) and its Relevance to Banking

Artificial Intelligence (AI) Applications in the Banking Industry

  • AML Pattern Detection
  • Chatbots
  • Algorithmic Trading
  • Fraud Detection

Impact of Artificial Intelligence on Investing

AI and its Impact on Finance Industry

Future Evolution of Business Analyst

  • How Will Business Analysis Evolve with Artificial Intelligence?

Hybrid Roles for Future Business Analyst

  • Business Analyst / Project Manager
  • Product Owner
  • Programmer / Analyst
  • Data Analyst
  • User Experience Designer (UX)

How Artificial Intelligence (AI) Change the Face of Business?

Co-Existence of Artificial Intelligence and Business Analysis

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Prerequisites

There are no prerequisites for this course.

Who should attend?

This course is designed for Business Analysts.

Artificial Intelligence (AI) for Business Analysts Course Overview

Artificial Intelligence (AI) is an area of computer science that focuses on the creation of intelligent machines that work and react like humans.

This Artificial Intelligence (AI) for Business Analysts training is designed to provide knowledge of how AI can help and enhance the skills of business analysts in making significant decisions for business. Delegates will learn the use of Artificial Intelligence (AI) in different fields like banking, finance, and investment and its impact on these.

During this course, delegates will be familiarised with different AI applications including AML pattern detection, Chatbots, Algorithmic Trading, and fraud detection. They will also acquire knowledge of various hybrid roles for future business analysts. On completion of this course, delegates will know how Artificial Intelligence (AI) can improve business processes.

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What's included?

  • The Knowledge Academy's Artificial Intelligence (AI) for Business Analysts Course Manual
  • Experienced Instructor
  • Certificate

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Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Artificial Intelligence (AI) for DevOps Course Outline

  • Introduction to Artificial Intelligence (AI)
  • Overview of DevOps
  • Role of AI in DevOps
  • Basics of Artificial Intelligence and DevOps Automation
  • Artificial Intelligence in DevOps Quality Assurance and Control
  • Selecting Tools of AI for DevOps Automation
  • AI for Team Coordination
  • Using AI for Continuous Integration, Delivery, and Deployment
  • Impact of AI on DevOps Culture
  • Improving DevOps with Artificial Intelligence
  • Applying Artificial Intelligence to DevOps Toolchain
  • Moving DevOps Team into AI
  • Artificial Intelligence Future with DevOps

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Prerequisites

There are no prerequisites for this course.

Who should attend?

This course is well suited for:

  • Application Developers
  • System Administrators
  • Security Engineers

Artificial Intelligence (AI) for DevOps Course Overview

Artificial Intelligence (AI) has an all-encompassing relationship with DevOps. Automating routine and repeatable actions is a fundamental facet of DevOps to help improve performance and productivity.

Artificial Intelligence reduces the operational complexities found in DevOps due to the highly distributed nature of the toolsets. AI can improve the automation quotient in DevOps by minimising the need for human involvement across processes.

This Artificial Intelligence for DevOps Training course is designed to provide knowledge of how AI is used in DevOps. During this training, delegates will become familiarised with AI and DevOps automation, including the use of AI for quality assurance and control. You will learn how AI impacts DevOps culture in general as well as its uses in delivery and deployment. Post completion of this course, you will be able to apply AI to the DevOps toolchain.

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What's included?

  • The Knowledge Academy's Artificial Intelligence (AI) for DevOps Course Manual
  • Experienced Instructor
  • Certificate

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Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Artificial Intelligence (AI) for IT Professionals Course Outline

Introduction to Artificial Intelligence (AI)

Building Blocks of AI

  • Machine Learning
  • Deep Learning

AI vs. Machine Learning vs. Deep Learning

How to train AI?

Implementing AI in an Organisation

  • Identify AI Opportunities
  • Develop an AI Roadmap
  • Identify AI Solutions
  • Identify Data Requirements

AI Use Cases in Information Management

Case Studies

(Human) Supervision of AI

Implementation Areas of AI

  • Voice Recognition
  • Natural Language Generation (NLG)
  • Virtual Assistants
  • Cognitive Computing
  • Computer Vision
  • Recommendation Systems
  • Natural Language Processing
  • Neural Networks
  • Robotic Process Automation
  • Machine Learning Platform
  • Predictive Analysis
  • Biometric Recognition
  • Image Analysis
  • Deep Learning Platforms
  • Quantum Computing
  • Decision Making and Management
  • Hardware Optimisation

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Prerequisites 

There are no prerequisites for attending this course. However, basic knowledge of statistics and programming will be beneficial.

Who should attend?

This course is designed for IT Professionals.

Artificial Intelligence (AI) for IT Professionals Course Overview

Artificial Intelligence (AI) is the science of creating machines that work intelligently. It is accomplished by analysing how the human brain functions during problem-solving and uses the results as the base of developing intelligent software and systems.

This Artificial Intelligence course for IT Professionals will provide delegates with an in-depth understanding of AI and its applications. Delegates will learn about the building blocks of AI and the differences between AI, machine learning, and deep learning.

In addition, this 1-day course will also provide delegates with knowledge on how to train AI. Delegates will become familiarised with AI use cases in information management and human supervision of AI. They will also learn how to implement AI in an organisation. By the end of this course, you will have learned about various implementation areas for AI including voice recognition, computer vision, neural networks, robotic process automation, and more.

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What's included

  • The Knowledge Academy's Artificial Intelligence (AI) for IT Professionals Training Course Manual
  • Experienced Instructor
  • Certificate

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Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Neural Networks with Deep Learning Training Course Outline

Introduction to Deep Learning

  • Introduction to Neural Networks
  • Supervised Learning with Neural Networks

Neural Networks Fundamentals

  • Binary Classification
  • Logistic Regression
  • Gradient Descent and Derivatives
  • Computational Graph
  • Vectorisation
  • Introduction to Python
  • Jupyter/IPython Notebooks

Shallow Neural Networks

  • Representation of a Neural Network
  • Computing the Output of a Neural Network
  • Vectorised Implementation
  • Activation Functions
  • Derivatives of Activation Functions
  • Gradient Descent for Neural Networks
  • Backpropagation Intuition

Deep Neural Networks

  • Deep L-layer Neural Network
  • Forward Propagation
  • Deep Representations
  • Building Blocks
  • Backward Propagation
  • Difference Between Parameters and Hyperparameters

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Prerequisites

There are no prerequisites for this course. However, some experience with the Python programming language would be beneficial.

Audience

Anyone who wants to learn about neural networks and deep learning can attend this course.

Neural Networks with Deep Learning Training Course Overview

Neural Networks are a set of algorithms designed to identify patterns. These are developed to imitate the human brain. Neural networks translate sensory data through labelling or clustering raw input and machine perception. These networks identify numerical patterns that are stored in vectors. All the real-world data, including text, images, or sound, must be translated into these numerical patterns. Neural networks can be thought of as a clustering and classification layer on top of the data stored and managed.

The Knowledge Academy’s Neural Networks with Deep Learning Training course will provide delegates with an understanding of deep learning and neural networks. Delegates will be familiarised with basic concepts of neural networks such as binary classification, logistic regression, derivatives, and vectorisation.

During this 1-day training course, delegates will be introduced to Python and Jupyter/IPython notebooks. Delegates will learn about shallow neural networks, including vectorised implementation, activation functions, and backpropagation intuition. In addition, delegates will also gain knowledge on the concepts of deep neural networks involving deep L-layer neural network, deep representations, and forward and backward propagation.

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  • Delegate pack consisting of course notes and exercises
  • Manual
  • Experienced Instructor
  • Refreshments

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Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Cognitive Computing Training​ Course Outline

Introduction to Cognitive Computing

  • Working of Human Brain
  • Artificial Neural Networks
  • Symbolic Representation of Facts and Rules
  • Symbolic Models for Natural Language Processing (NLP)

Basics of Linguistics

  • Phonology
  • Morphology
  • Syntax
  • Semantics
  • Pragmatics

Cognitive Computing – Practical Applications

Introduction to Machine Learning

  • Supervised and Unsupervised Learning
  • Linear Regression
  • Backpropagation Neural Networks
  • Feature Engineering

TensorFlow for Implementing Deep Neural Networks

  • Installing TensorFlow
  • Convolutional Deep Learning Networks for Text Classification

Tools and Techniques – Natural Language Processing

  • Overview of spaCy NLP Library
  • spaCy for Assigning Part of Speech Tags
  • spaCY for Entity Recognition
  • Overview of OpenNLP Library and Installation Notes

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Prerequisites

There are no prerequisites for attending this course.

Audience

Anyone interested in learning how cognitive computing works with machine learning, deep neural networks, and NLP can attend this course.

 

Cognitive Computing Training​ Course Overview

Cognitive Computing simulates the thought processes of humans. It uses deep-learning or self-learning algorithms backed by natural language processing, big data, and artificial intelligence. Cognitive Computing solves complicated problems characterised by uncertainty and ambiguity. It synthesises data from different information sources, while weighing context and conflicting evidence to advise the best possible answers.

This Cognitive Computing Training is designed to equip delegates with in-depth knowledge on how Cognitive Computing works. Delegates will learn about artificial neural network and symbolic representation of facts and rules. During this 1-day training course, delegates will become familiarised with the basics of linguistics. In addition, delegates will gain knowledge of supervised learning, unsupervised learning, and linear regression. Post completion of this training, delegates will be able to use spaCy for assigning part of speech tags and entity recognition.

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  • Delegate pack consisting of course notes and exercises
  • Manual
  • Experienced Instructor
  • Refreshments

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Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Recommendation System Training Course Outline

Introduction to Recommender Systems

  • What is Recommender System?
  • Goals of Recommender System
  • Basic Models of Recommender System
  • Domain-Specific Challenges

Collaborative Recommendation

  • User and Item Based Neighbor Recommendation
  • Model-Based and Preprocessing-Based Approaches
  • Practical Approaches and Systems

Knowledge-Based Recommendation

  • Knowledge Representation and Reasoning
  • Interacting with Constraint-Based Recommenders
  • Interacting with Case-Based Recommenders

Hybrid Recommendation Approaches

  • Opportunities for Hybridisation
  • Monolithic Hybridisation Design
  • Parallelised Hybridisation Design
  • Pipelined Hybridisation Design

Recommender Systems Explanations

  • Constraint-Based Recommenders
  • Case-Based Recommenders
  • Collaborative Filtering Recommenders

Evaluating Recommender Systems

  • Properties of Evaluation Research
  • Popular Evaluation Designs
  • Evaluation of Historical Datasets
  • Alternate Evaluation Designs

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Prerequisites

There are no prerequisites to attend this course.

Audience

Anyone wishes to have a comprehensive knowledge of Recommendation system can attend this course. This course is well-suited for:

  • Machine Learning Engineers
  • Data engineers and Scientists
  • Data Analysts

Recommendation System Training​ Course Overview

A Recommendation system is an extensive class of web applications comprising predicting the user responses to the options. It is a data filtering tool that analyses historical data for predicting what users will be interested in and create accurate recommendations. This system is mostly used in social media, e-commerce platforms, and content-based services. This Recommendation System Training is designed to equip delegates with a knowledge of all the fundamental techniques in the recommender system.

In this Recommendation System Training, delegates will learn about basic concepts of recommendation systems. Delegates will get an understanding of model-based and preprocessing-based approaches. In addition, delegates will learn how to interact with constraint and case-based recommenders.

During this 1-day training, delegates will gain extensive knowledge of hybrid recommendation approaches. This course will introduce delegates to explanations in constraint, case, and collaborative based recommenders. Post completion of this course, delegates will be able to evaluate recommender systems.

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  • Delegate pack consisting of course notes and exercises
  • Manual
  • Experienced Instructor

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Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

AI and ML with Excel Training Course Outline

Module 1: Introducing AI in MS Excel

  • What is AI for Excel?
  • Intelligent Suggestions with Ideas
  • Making New Data Types
  • Availability
  • Preparing Data
  • Running Insights
  • Improving Machine Learning

Module 2: Machine Learning with Excel

  • Training Set Vs Test Set
  • Classification Models
  • Preparing Data in Excel
  • Building the Model
    • Calculating Distance
    • Finding Nearest Neighbour
  • Set Up and Running Algorithm

Module 3: Smart Spreadsheets

  • Artificial Intelligence Based Features in Excel
  • Benefits of Smart Sheets
  • Why Rollback?

Module 4: Dynamic Arrays in Excel

  • Introduction to Dynamic Arrays
  • Dynamic Arrays Formula
    • UNIQUE
    • SORT
    • SORT BY
    • SEQUENCE
    • RANDARRAY
    • FILTER
    • LOOKUP

Module 5: Automated Text Analysis Using AI in Excel

  • What is Text Analysis?
  • How Can Text Analysis Help?
  • How to Use Text Analysis Tools in Excel?
  • Create Text Analysis Model
  • Text Analysis Use Cases and Applications

Module 6: Linear Regression Analysis in Excel

  • Introduction to Linear Regression
  • How to Add Linear Regression Data Analysis Tool in Excel?
  • Methods for Using Linear Regression in Excel
    • Scatter Chart with a Trendline
    • Analysis ToolPak Add-In Method
  • How to Do Regression in Excel Using Formulas?

Module 7: Cluster Analysis in Excel

  • Steps to Run K-Means Cluster Analysis
    • Start with Dataset
    • Use Scatter Graph
    • Calculate Distance from Each Data Point
    • Calculate Mean (Average) of Each Cluster Set
    • Distance from the Revised Mean
    • Graph and Summarise the Clusters 

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Prerequisites

There are no formal prerequisites for attending this AI and ML with Excel Training course. However, a basic understanding of Microsoft Excel and Artificial Intelligence would be beneficial for delegates.

Audience

The Knowledge Academy designed this training course for those who wish to enhance their knowledge of Artificial Intelligence and Machine Learning using Microsoft Excel.

AI and ML with Excel Training Course Overview

Artificial Intelligence (AI) is a broad field of computer science that builds intelligent computers that can carry out tasks that traditionally require human intelligence. The ideal AI quality is the ability to rationally take actions that have the best chance of achieving a specific goal. Studying this training assists aspiring candidates in elevating Microsoft Excel to reduce human efforts in managing and analysing Excel data using AI and ML. This training aims to provide organisations with techniques for effectively and seamlessly automating Excel data handling. Individuals with excellent AI and ML skills will get higher designations in globally recognised organisations and claim their desired earnings.

In this 1-day AI and ML with Excel Training, delegates will gain comprehensive knowledge of using AI and ML features in Microsoft Excel for performing various tasks. During this training, delegates will learn about smart spreadsheets that provide insights for quantitative and visual results. They will also learn about text analysis in Excel that automates the process of extracting and classifying data using AI. Our highly expert and professional instructor, with years of experience in teaching technical courses, will conduct this training.

Course Objectives

  • To build data presentation within an Excel spreadsheet
  • To save time and reduce the chances of errors occurring
  • To create dataset that help to achieve data science objectives
  • To produce professional spreadsheets and documents in less time
  • To learn from patterns in the data to form a result in native process
  • To integrate data yielding visualisation of data in sortable complex arrays

After completing this training, delegates will be able to access multiple values with one formula and build spreadsheets using fewer formulas. They will also be able to predict the values of dependent variables and relationship between both dependent and independent variables.

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  • Delegate pack consisting of course notes and exercises
  • Courseware
  • Experienced Instructor

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Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

OpenAI Training Course Outline

Module 1: Introduction to OpenAI

  • What is OpenAI?
  • Start with an Instruction
  • Add Examples
  • Adjust Settings
  • Build Application
  • Libraries
  • Models
  • Usage Policies

Module 2: Text Completion

  • Introduction
  • Prompt Design
  • Inserting Text
  • Editing Text

Module 3: Code Completion

  • Introduction
  • Best Practices
  • Inserting Code
  • Editing Code

Module 4: Image Generation

  • Usage
  • Language-Specific Tips

Module 5: Fine-Tuning

  • Preparing Dataset
  • Advanced Usage
  • Weights and Biases

Module 6: Embeddings

  • What are Embeddings?
  • How to Get Embeddings?
  • Embedding Models
  • Use Cases
  • Limitations and Risks

Module 7: Moderation

  • Overview
  • Quickstart

Module 8: Rate Limits

  • What are Rate Limits?
  • Why Do We Have Rate Limits?
  • Rate Limits of Our API
  • How do Rate Limits Work?
  • Rate Limit Errors
  • Error Mitigation
  • Request Increase

Module 9: Safety Best Practices

  • Use Moderation API
  • Adversarial Testing
  • Human in the Loop (HITL)
  • Prompt Engineering
  • Know Your Customer (KYC)
  • Constrain User Input and Limit Output Tokens
  • Allow Users to Report Issues
  • Understand and Communicate Limitations
  • End-User IDs

Module 10: Production Best Practices

  • Setting Up Your Organisation
  • Building Your Prototype
  • Evaluation and Iteration
  • Scaling Your Solution
  • Managing Rate Limits
  • Improving Latencies
  • Managing Costs
  • MLOps Strategy
  • Security and Compliance

 

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Prerequisites

There are no formal prerequisites to attend this OpenAI Training course.

Audience

This OpenAI Training course is suitable for anyone who wants to develop friendly AI in a responsible manner. 

OpenAI Training Course Overview

The OpenAI API can be used to do any activity that includes understanding or producing natural language or code. It provides a range of models with varying degrees of power appropriate for various activities and the option to fine-tune unique models. OpenAI leverages a spectrum of models used for everything from content generation to semantic search and classification. This training will enable individuals to generate and analyse written sentences in various ways while understanding the relationship between translations and variations. Individuals with excellent programming skills will get higher designations in globally recognised organisations and claim their desired earnings.

This 1-day OpenAI Training course teaches delegates how to solve a task that involves processing language. During this training course, they will learn how to improve the other models’ performance by fine-tuning them for a specific task. They will also learn how to use APIs safely and responsibly through usage policies. Our highly expert and professional instructor, with years of experience in teaching technical courses, will conduct this training.

Course Objectives

  • To manipulate images based on a new text prompt
  • To generate a text completion to match the given pattern
  • To reduce the occurrence of unsafe content in completions
  • To maintain a smooth and consistent experience for all users
  • To customise a model for an application with high-quality results
  • To learn how to recover from rate limit errors without missing data or crashing

After completing this training course, delegates will be able to use OpenAI for identifying users and detecting any policy violations in their applications. They will also be able to mitigate misalignment between the back-end-offered API and the client-consumed API.

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  • Delegate pack consisting of course notes and exercises
  • Courseware
  • Experienced Instructor

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Not sure which course to choose?

Speak to a training expert for advice if you are unsure of what course is right for you. Give us a call on 01344203999 or Enquire.

Package deals

Our training experts have compiled a range of course packages to compliment a variety of categories in order to help fast track your career. The packages consist of the best possible qualifications in each industry and allows you to purchase multiple courses at a discounted rate.

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Artificial Intelligence & Machine Learning FAQs

FAQ's

This depends on the training course you choose. The introduction to Artificial Intelligence and Deep Learning Training with TensorFlow takes 1 day, and the Machine Learning Training Course and NLP fundamentals with Python Essential Training lasts 2 days.
We offer Artificial Intelligence and Machine Learning training courses in locations all over the UK, as well as abroad. We make it easy to find a training venue near you!
The Knowledge Academy’s Artificial Intelligence and Machine Learning training courses include the courseware, a certificate, an experienced instructor and, refreshments.
Prerequisites vary according to the course. Please see each course for details.
The Knowledge Academy is the Leading global training provider for Artificial Intelligence & Machine Learning.
The price for Artificial Intelligence & Machine Learning certification in the United Kingdom starts from £.

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