Artificial Intelligence & Machine Learning - United Kingdom

Artificial Intelligence & Machine Learning

The Artificial Intelligence and Machine Learning Course explores the fundamentals of AI, machine learning algorithms, and their real-world applications. Delegates will gain hands-on experience with data analysis, and implementing AI solutions across various industries.

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Learn to apply industry-relevant knowledge in Machine Learning and Artificial Intelligence (AI)
Enhance your skill set with our Artificial Intelligence and Machine Learning Courses
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Artificial Intelligence & Machine Learning

Duration

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
- Types of Artificial Intelligence
- Various Kinds of Technologies
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

New Foundation
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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Who should attend this Introduction to Artificial Intelligence Training Course?

The Introduction to Artificial Intelligence Course is a comprehensive course designed to equip participants with the foundational knowledge and skills required to understand, adapt, and harness future AI technologies. The following professionals can benefit from this course:

Software Developers
Data Analysts
Business Analysts
Healthcare Practitioners
Marketing Professionals
Data Scientists
Financial Analysts

Prerequisites of the Introduction to Artificial Intelligence Training Course

There are no formal prerequisites for Introduction to Artificial Intelligence Course.

Introduction to Artificial Intelligence Course Overview

Artificial Intelligence (AI) stands at the forefront of technological innovation, reshaping industries and enhancing human capabilities. An Introduction to AI offers a foundational understanding of this dynamic field, emphasising its potential to solve complex problems and optimise processes. This Artificial Intelligence Course caters to beginners eager to understand the basics of AI and its applications.

Understanding AI is crucial for professionals across various sectors, including technology, healthcare, finance, and education, who seek to leverage AI for strategic advantage. Mastery of this subject is especially beneficial for data scientists, software engineers, and business analysts aiming to implement AI solutions in their operations. The Artificial Intelligence Training equips these professionals with the necessary skills to innovate and stay competitive.

The 1-day training provided by the Knowledge Academy is designed to empower delegates with a concise yet comprehensive overview of AI. Delegates will gain insights into AI concepts, tools, and real-world applications through expert-led sessions and hands-on exercises. This training prepares individuals to understand and engage with Artificial Intelligence, boosting their proficiency and confidence in navigating this rapidly evolving field.

Course Objectives

To introduce the foundational principles and technologies underpinning Artificial Intelligence
To illustrate practical applications of AI in various industries
To develop critical thinking skills for AI problem-solving and innovation
To enhance understanding of AI ethics and societal impacts
To prepare delegates for further advanced studies or certifications in AI

Upon completion of this Artificial Intelligence Course, delegates will possess a solid understanding of key AI concepts and applications. They will be equipped to recognize opportunities for AI integration in their respective fields and initiate AI-driven projects with a foundational knowledge base.

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What’s included in this Introduction to Artificial Intelligence Training Course?

World-Class Training Sessions from Experienced Instructors
Introduction to Artificial Intelligence Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Machine Learning Course Outline

Module 1: Machine Learning - Introduction

What is Machine Learning?
Main Elements of Machine Learning
Traditional Programming Vs Machine Learning
Real Time Applications of Machine Learning

Module 2: Importance of Machine Learning and its Techniques

Importance of Machine Learning
Types of Machine Learning
How Machine Learning Works?

Module 3: Machine Learning Mathematics

What is Machine Learning Mathematics?
Why Mathematics is Significant for Machine Learning?

Module 4: Data Pre-Processing

What is Data Pre-Processing?
Way to Handling Missing Values

Module 5: Supervised Learning

Introduction to Supervised Learning

Module 6: Classification

Introduction to Classification
Types of Learners
Support Vector Machines (SVM)
How does SVM Work?
Discriminant Analysis
Naive Bayes
Nearest Neighbour

Module 7: Regression

Introduction to Regression
Regression Models
Linear Regression and GLM
SVR
Decision Tree
Neural Networks

Module 8: Unsupervised Learning

What is Unsupervised Learning?
Difference Between Supervised and Unsupervised Learning

Module 9: Clustering

Introduction to Clustering
K-Means
K-Medoids
Fuzzy
Hierarchal
Gaussian Mixture
Hidden Markov Model

Module 10: Deep Learning

Introduction to Deep Learning
Importance of Deep Learning
How Deep Learning Works?

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Who should attend this Machine Learning Training Course?

The Machine Learning Course is an intensive and comprehensive course designed to provide a deep dive into the fundamental concepts and applications of Machine Learning. The following are some professionals who can benefit greatly from this course:

Data Scientists
Data Analysts
Software Engineers
Business Analysts
Operations Managers
HR Professionals
Project Managers
Customer Service Managers

Prerequisites of the Machine Learning Training Course

Delegates must have a basic understanding of Python Programming and Statistics.

Machine Learning Course Overview

Embark on an immersive exploration of Machine Learning, a transformative field at the intersection of computer science and artificial intelligence. As the digital landscape evolves, the relevance of Machine Learning in extracting insights from data and powering intelligent systems becomes increasingly vital.

Mastery of Machine Learning is imperative for professionals in data science, software development, and business analytics. Those aspiring to harness the potential of data for informed decision-making should aim to master Machine Learning techniques. The Machine Learning Course is tailored for individuals seeking to elevate their analytical skills and stay ahead in an era driven by data-driven innovations.

The Knowledge Academy's 1-day Machine Learning Course equips delegates with practical knowledge and hands-on experience in deploying Machine Learning algorithms. The training delves into the essentials of data analysis, model building, and predictive analytics, ensuring participants gain a comprehensive understanding of Machine Learning applications. By the end of the course, delegates will be well-versed in leveraging Machine Learning tools to extract meaningful insights and drive informed decision-making.

Course Objectives

To comprehend the fundamental principles of Machine Learning for data-driven insights
To understand the significance of Machine Learning in enhancing analytical and predictive capabilities
To gain hands-on experience in deploying Machine Learning algorithms for real-world applications
To enhance analytical skills through practical application of Machine Learning concepts
To empower professionals to leverage data effectively for informed decision-making
To stay ahead in the dynamic landscape of data-driven innovations

Upon completion, delegates will benefit from enhanced analytical skills and a deep understanding of Machine Learning applications. They will be equipped to apply Machine Learning techniques to real-world scenarios, extracting valuable insights from data and driving informed decision-making in their respective professional domains.

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What’s included in this Machine Learning Training Course?

World-Class Training Sessions from Experienced Instructors
Machine Learning Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Deep Learning Course Outline

Module 1: Machine Learning Basics

What is Machine Learning?
Need for Machine Learning
Types of Machine Learning

Module 2: Introduction to Deep Learning

Importance of Deep Learning
How Deep Learning Works?
Difference Between Deep Learning and Machine Learning

Module 3: Artificial Neural Network

Introduction
Characteristics of Artificial Neural Network

Module 4: Deep Neural Networks

Feedforward Networks
Convolutional Neural Networks
Recurrent Neural Networks

Module 5: Linear Algebra

Mathematical Objects
Computational Rules in Linear Algebra
Matrix Multiplication Properties

Module 6: Probability

Terminology
Random Variables
Probability Distributions
Marginal Probability
Conditional Probability
Chain Rule of Conditional Probabilities
Baye’s Rule

Module 7: Auto Encoders

Need of Auto Encoder
Architecture of Auto Encoders
Applications of Auto Encoders

Module 8: Computational Graphs

What are Computational Graphs?

Module 9: Monte Carlo Methods

Introduction
Machine Learning in Monte Carlo Method

Module 10: Deep Generative Models

Introduction
Boltzmann Machines
- Functioning of Boltzmann Machines

Module 11: Deep Learning Applications

Applications of Deep Learning

Module 12: Libraries and Frameworks

Libraries
Framework
- Features of Deep Learning Framework

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Who should attend this Deep Learning Course?

This Deep Learning Training Course aims at equipping individuals with knowledge of Natural Language Processing, Robotics, and even Healthcare. This course will teach Deep Learning algorithms, technologies, and applications providing learners the skills needed to implement and adapt Deep Learning models for different tasks. This course can be beneficial for a wide range of professionals, including:

Data Scientists
Machine Learning Engineers
Research Scientists
Software Developers
Artificial Intelligence (AI)/Machine Learning (ML) Product Managers
Business Analysts
Finance Professionals

Prerequisites of the Deep Learning Course

To attend this Deep Learning Training Course, delegates should have a basic understanding of Python, Linear Algebra, and Probability.

Deep Learning Course Overview

Deep Learning is a subset of Artificial Intelligence (AI) that focuses on algorithms inspired by the structure and function of the human brain's neural networks. It's pivotal in revolutionising industries like healthcare, finance, and technology by enabling machines to learn from data, recognise patterns, and make intelligent decisions autonomously.

Proficiency in Deep Learning Course is crucial for Data Scientists, AI Engineers, Software Developers, and Researchers. Mastering this field empowers professionals to create innovative image and speech recognition solutions, natural language processing, autonomous vehicles, and predictive analytics. It is essential for those aiming to stay competitive and drive technological advancements across various industries.

This intensive 1-day course equips delegates with fundamental concepts and practical skills in deep learning. Through hands-on workshops and expert-led sessions, delegates comprehensively understand neural networks, convolutional and recurrent neural networks, and their applications. Delegates learn to implement deep learning models, interpret results, and optimise algorithms for diverse real-world scenarios.

Course Objectives

To understand the foundational principles of neural networks
To explore various deep learning architectures, including CNNs and RNNs
To apply Deep Learning algorithms in image and speech recognition tasks
To analyse and interpret profound learning model results effectively
To optimise and fine-tune neural networks for improved performance
To comprehend ethical considerations in deploying deep learning solutions
To develop practical skills through hands-on exercises and case studies
To foster confidence in applying deep learning techniques in real-world projects

After completing the course, delegates receive a certification validating their proficiency in deep learning fundamentals. This certification attests to their understanding of neural network concepts, ability to design and implement deep learning models, and skills in utilising these techniques to solve practical problems effectively.

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What’s included in this Deep Learning Course?

World-Class Training Sessions from Experienced Instructors
Deep Learning Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Deep Learning with TensorFlow Course Outline

Module 1: Introduction to TensorFlow

Tensors
TensorFlow
Installation of TensorFlow
Two Computation Phrases
Variables
Operations
A Computational Graph with TensorBoard
Linear Regression

Module 2: Artificial Neural Network

Introduction
Characteristics of Artificial Neural Network

Module 3: Activate Functions

Introduction Activation (Transfer) Functions
Types of Activate Functions
Unit Step (Threshold)
Sigmoid
Piecewise Linear
Gaussian
Linear

Module 4: Deep Learning Techniques

Introduction
Convolutional Neural Networks
Recurrent Neural Networks

Module 5: Deep Learning Applications

Applications of Deep Learning

Module 6: Computing Gradients

Introduction
Steps for Computing Gradients

Module 7: Single-Layer and Multi-Layer Perceptron

Perceptron
- Single-Layer Perceptron
- Multi-Layer Perceptron (MLP)

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Who should attend this Deep Learning with TensorFlow Training Course?

The Deep Learning with TensorFlow Course is a specialised course focused on advanced Machine Learning techniques using TensorFlow, one of the most widely used open-source libraries for numerical computation and Machine Learning. The following professionals will benefit greatly from this course:

Machine Learning Engineers
Data Scientists
Artificial Intelligence (AI) Researchers
Software Developers
Natural Language Processing Engineers
Automotive Engineers
Robotics Engineers

Prerequisites of the Deep Learning with TensorFlow Training Course

Delegates should have a basic understanding of Python Programming and Machine Learning.

Deep Learning with TensorFlow Course Overview

Deep Learning with TensorFlow is a comprehensive course designed to teach professionals how to build and deploy deep learning models using the TensorFlow framework. This course is essential for those looking to harness the power of neural networks to solve complex problems and drive innovation in fields like healthcare, finance, and technology.

Proficiency in TensorFlow and deep learning is crucial for Data Scientists, AI Engineers, Software Developers, and Researchers. Mastering this field empowers professionals to develop cutting-edge solutions in image and speech recognition, natural language processing, autonomous systems, and predictive analytics. It is vital for anyone aiming to stay competitive and lead technological advancements in their industry.

This intensive 1-day course equips delegates with a thorough understanding of deep learning concepts and practical skills using TensorFlow. Through hands-on workshops and expert-led sessions, delegates gain insights into neural network architectures, including convolutional and recurrent neural networks, and their applications. Delegates learn to implement deep learning models, interpret their results, and optimise them for various real-world scenarios.

Course Objectives

To understand the foundational principles of neural networks
To explore TensorFlow and its capabilities in building deep learning models
To apply deep learning algorithms in image and speech recognition tasks
To analyse and interpret deep learning model results effectively
To optimise and fine-tune neural networks for improved performance
To comprehend ethical considerations in deploying deep learning solutions

Upon completing this course, delegates will have acquired the knowledge and skills necessary to implement and optimise deep learning models using TensorFlow, making them invaluable assets in their professional fields.

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What’s included in this Deep Learning with TensorFlow Training Course?

World-Class Training Sessions from Experienced Instructors
Deep Learning with TensorFlow Certificate
Digital Delegate Pack

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Duration

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 Modelling

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 Labelling
Language Modelling
Neural Machine Translation
Sentiment Analysis
Text Summarisation

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Who should attend this Natural Language Processing (NLP) Fundamentals with Python Course?

The Natural Language Processing (NLP) Fundamentals with Python Course can be beneficial for a wide range of individuals who are interested in understanding and working with text data. The following are some professionals who can benefit from this course:

Software Developers
Data Scientists
Machine Learning Engineers
Data Analysts
Artificial Intelligence (AI) Researchers
Product Managers
Business Analysts

Prerequisites of the Natural Language Processing (NLP) Fundamentals with Python Course

Delegates should have a basic knowledge and understanding of Python.

Natural Language Processing (NLP) Fundamentals with Python Course Overview

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that focuses on the interaction between computers and human language. It is pivotal in transforming industries like customer service, healthcare, and finance by enabling machines to understand, interpret, and generate human language in a valuable way.

Proficiency in NLP with Python is crucial for Data Scientists, AI Engineers, Software Developers, and Linguists. Mastering this field empowers professionals to create innovative text analysis solutions, sentiment analysis, language translation, and chatbots. It is essential for those aiming to stay competitive and drive advancements in technology across various sectors.

This intensive 2-day course equips delegates with fundamental concepts and practical skills in natural language processing using Python. Through hands-on workshops and expert-led sessions, delegates comprehensively understand text preprocessing, tokenisation, and sentiment analysis. Delegates learn to implement NLP models, interpret results, and optimise algorithms for diverse real-world scenarios.

Course Objectives

To understand the foundational principles of natural language processing
To explore various NLP techniques, including text preprocessing and tokenisation
To apply NLP algorithms in sentiment analysis and language translation tasks
To analyse and interpret NLP model results effectively
To optimise and fine-tune NLP algorithms for improved performance
To comprehend ethical considerations in deploying NLP solutions

Upon completing this course, delegates will have acquired the knowledge and skills necessary to implement and optimise NLP models using Python, making them invaluable assets in their professional fields.

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What’s included in this Natural Language Processing (NLP) Fundamentals with Python Course?

World-Class Training Sessions from Experienced Instructors
Natural Language Processing (NLP) Fundamentals with Python Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Artificial Intelligence (AI) for Project Managers Course Outline

Module 1: Introduction to Artificial Intelligence

Overview
Aim and Purpose
Scope of Research
AI Within the Corporate Context

Module 2: Theoretical Framework

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

Module 3: Methodology

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

Module 4: Results – Surveys

Introduction
Artificial Intelligence
Project Management
Organisational Business

Module 5: Results – Interviews

Interviews
Project Management
Artificial Intelligence

Module 6: 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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Who should attend this Artificial Intelligence (AI) for Project Managers Course?

The Artificial Intelligence (AI) for Project Managers Training Course is tailored for individuals working in the Business Analysis field to help them analyse business operations thoroughly and help in strategy development. The following are some professionals for whom this course can be beneficial:

Business Analysts
Data Analysts
Project Managers
Product Managers
UX/UI Designers
Software Engineers
Operations Managers

Prerequisites of the Artificial Intelligence (AI) for Project Managers Course

There are no formal prerequisites for this Artificial Intelligence (AI) for Project Managers Course.

Artificial Intelligence (AI) for Project Managers Course Overview

Artificial Intelligence (AI) is revolutionising project management by automating routine tasks, improving decision-making, and enhancing overall project efficiency. AI technologies such as machine learning, natural language processing, and predictive analytics empower project managers to plan, execute, and deliver projects more effectively by leveraging data-driven insights and intelligent automation.

Proficiency in AI is crucial for Project Managers, Programme Managers, PMO Directors, and other professionals involved in project delivery. Mastering AI tools and techniques enables these professionals to streamline project workflows, foresee risks, optimise resource allocation, and improve stakeholder communication. It is essential for those aiming to stay competitive and drive innovation in project management practices across various industries.

This intensive 1-day course equips delegates with fundamental concepts and practical skills in applying AI to Project Management. Through hands-on workshops and expert-led sessions, delegates gain a comprehensive understanding of AI applications in project management, including task automation, predictive analytics, and intelligent decision support systems. Delegates learn to implement AI-driven tools, interpret AI insights, and integrate AI solutions into their project management processes effectively.

Course Objectives

To understand the foundational principles of AI and its relevance to Project Management
To explore various AI tools and techniques used in automating Project Management tasks
To apply machine learning algorithms for predictive project analytics
To analyse and interpret AI-generated insights for improved decision-making
To optimise project workflows and resource management using AI technologies
To comprehend ethical considerations in deploying AI solutions in Project Management

Upon completing this course, delegates will have acquired the knowledge and skills necessary to implement and optimise AI-driven Project Management practices, making them invaluable assets in their professional fields.

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What’s included in this Artificial Intelligence (AI) for Project Managers Course?

World-Class Training Sessions from Experienced Instructors
Artificial Intelligence (AI) for Project Managers Certificate
Digital Delegate Pack

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More information

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Duration

Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Artificial Intelligence (AI) for Business Analysts Course Outline

Module 1: Introduction to Artificial Intelligence

Overview
Need for Artificial Intelligence
AI Approaches

Module 2: Use of Artificial Intelligence (AI)

In Banking and In Finance
In Investment

Module 3: AI and Its Relevance to Banking

Overview
How is AI Firming Up the Competitiveness of Banks?

Module 4: AI Applications in the Banking Industry

AML Pattern Detection
Chatbots
Algorithmic Trading
Fraud Detection

Module 5: Impact of Artificial Intelligence on Investing

Overview
How can You Use AI and ML for Trading/Investing?

Module 6: AI and Its Impact on Finance Industry

Introduction

Module 7: Future Evolution of Business Analyst

Introduction
How Will Business Analysis Evolve with Artificial Intelligence?

Module 8: Hybrid Roles for Future Business Analyst

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

Module 9: How AI Change the Face of Business?

Overview
Superior Enterprise Mobility Through AI
Marketing and Advertising
Increased Efficiency and Higher Precision at Lower Costs
Help to Integrate and Consolidate Business Operations
Stronger Cyber Security

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Who should attend this Artificial Intelligence (AI) for Business Analysts Course?

The Artificial Intelligence (AI) for Business Analysts Training Course is tailored for individuals working in the Business Analysis field to help them analyse business operations thoroughly and aid in strategy development. The following are some professionals for whom this course can be beneficial:

Business Analysts
Data Analysts
Project Managers
Product Managers
UX/UI Designers
Software Engineers
Operations Managers

Prerequisites of the Artificial Intelligence (AI) for Business Analysts Course

There are no formal prerequisites for this Artificial Intelligence (AI) for Business Analysts Training Course.

Artificial Intelligence (AI) for Business Analysts Course Overview

Artificial Intelligence (AI) is revolutionising business analysis by automating routine tasks, improving decision-making, and enhancing overall operational efficiency. AI technologies such as machine learning, natural language processing, and predictive analytics empower business analysts to analyse data, derive insights, and develop strategies more effectively by leveraging data-driven insights and intelligent automation.

Proficiency in AI is crucial for Business Analysts, Data Analysts, Project Managers, and other professionals involved in business operations. Mastering AI tools and techniques enables these professionals to streamline analytical workflows, foresee market trends, optimise resource allocation, and improve stakeholder communication. It is essential for those aiming to stay competitive and drive innovation in business analysis practices across various industries.

This intensive 1-day course equips delegates with fundamental concepts and practical skills in applying AI to Business Analysis. Through hands-on workshops and expert-led sessions, delegates gain a comprehensive understanding of AI applications in business analysis, including data automation, predictive analytics, and intelligent decision support systems. Delegates learn to implement AI-driven tools, interpret AI insights, and integrate AI solutions into their business analysis processes effectively.

Course Objectives

To understand the foundational principles of AI and its relevance to Business Analysis
To explore various AI tools and techniques used in automating Business Analysis tasks
To apply machine learning algorithms for predictive analytics
To analyse and interpret AI-generated insights for improved decision-making
To optimise business workflows and resource management using AI technologies
To comprehend ethical considerations in deploying AI solutions in Business Analysis

Upon completing this course, delegates will have acquired the knowledge and skills necessary to implement and optimise AI-driven Business Analysis practices, making them invaluable assets in their professional fields.

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What’s included in this Artificial Intelligence (AI) for Business Analysts Course?

World-Class Training Sessions from Experienced Instructors
Artificial Intelligence (AI) for Business Analysts Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Artificial Intelligence (AI) for DevOps Course Outline

Module 1: Introduction to Artificial Intelligence (AI)

What is Artificial Intelligence (AI)?
Business Benefits of AI
Why is Artificial Intelligence Important?
Artificial Intelligence Use Cases
DevOps Automation is an Ideal Use Case for Artificial Intelligence (AI)
Artificial Intelligence and DevOps Automation
AI in DevOps Quality Assurance and Control

Module 2: Overview of DevOps

Challenges of Scaling DevOPS
Objectives of DevOps
Phases of DevOps Maturity
Values of DevOps
What Tools are Used in DevOps?

Module 3: AI Tools for DevOps Automation

AI for DevOps Automation Software Stacks
Future of AI and DevOps Automation
Required System for DevOps Automation

Module 4: Power of AI in DevOps

Challenges in DevOps
Intelligent Release Orchestration and Management
Benefits of DevOps with AI
DevOps and AI Work Together

Module 5: Ways AI is Transforming DevOps

How DevOps and AI Operate Together?
Ways Artificial Intelligence is Transforming DevOps
- Software Testing
- Improved Data Access
- Time Alerts
- Superior Execution Efficiency
- Swifter Failure Forecasting
- Smarter Resource Management
- Faster Root Cause Analysis
- Feedback Loop
- Anomaly Detection
- More Efficient Collaboration

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Who should attend this Artificial Intelligence (AI) for DevOps Course?

The Artificial Intelligence (AI) for DevOps Training Course is tailored for individuals working in the DevOps field to help them enhance their operations and improve efficiency through AI technologies. The following are some professionals for whom this course can be beneficial:

DevOps Engineers
Systems Administrators
Cloud Engineers
Software Developers
IT Managers
Operations Managers
Infrastructure Engineers

Prerequisites of the Artificial Intelligence (AI) for DevOps Course

There are no formal prerequisites for this Artificial Intelligence (AI) for DevOps Training Course.

Artificial Intelligence (AI) for DevOps Course Overview

Artificial Intelligence (AI) is revolutionising DevOps by automating routine tasks, improving decision-making, and enhancing overall operational efficiency. AI technologies such as machine learning, natural language processing, and predictive analytics empower DevOps professionals to plan, execute, and manage operations more effectively by leveraging data-driven insights and intelligent automation.

Proficiency in AI is crucial for DevOps Engineers, Systems Administrators, IT Managers, and other professionals involved in DevOps processes. Mastering AI tools and techniques enables these professionals to streamline operations, foresee risks, optimise resource allocation, and improve system reliability. It is essential for those aiming to stay competitive and drive innovation in DevOps practices across various industries.

This intensive 1-day course equips delegates with fundamental concepts and practical skills in applying AI to DevOps. Through hands-on workshops and expert-led sessions, delegates gain a comprehensive understanding of AI applications in DevOps, including task automation, predictive analytics, and intelligent decision support systems. Delegates learn to implement AI-driven tools, interpret AI insights, and integrate AI solutions into their DevOps processes effectively.

Course Objectives

To understand the foundational principles of AI and its relevance to DevOps
To explore various AI tools and techniques used in automating DevOps tasks
To apply machine learning algorithms for predictive analytics in DevOps
To analyse and interpret AI-generated insights for improved decision-making
To optimise operational workflows and resource management using AI technologies
To comprehend ethical considerations in deploying AI solutions in DevOps

Upon completing this course, delegates will have acquired the knowledge and skills necessary to implement and optimise AI-driven DevOps practices, making them invaluable assets in their professional fields.

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What’s included in this Artificial Intelligence (AI) for DevOps Course?

World-Class Training Sessions from Experienced Instructors
Artificial Intelligence (AI) for DevOps Certificate
Digital Delegate Pack

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More information

See dates & prices

Duration

Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Artificial Intelligence (AI) for IT Professionals Course Outline

Module 1: Introduction to Artificial Intelligence (AI).

What is Artificial Intelligence?

Module 2: Building Blocks of AI.

Machine Learning.
Deep Learning.

Module 3: AI vs Machine Learning vs Deep Learning.

Difference Between AI, Machine Learning, and Deep Learning.

Module 4: How to Train AI?

Overview.
Steps in the Process of AI Training.

Module 5: Implementing AI in an Organisation.

Identify AI Opportunities.
Develop an AI Roadmap.
Identify AI Based Solutions.
Identify Data Requirements.

Module 6: AI Use Cases in Information Management.

Supervision of AI.
Implementation Areas of AI.
Cognitive Computing.

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Who should attend this Artificial Intelligence (AI) for IT Professionals (AI4IT) Course?

The Artificial Intelligence (AI) for IT Professionals (AI4IT) Course is tailored for individuals working in the IT field to help them integrate AI technologies into their workflows and enhance system efficiencies. The following are some professionals for whom this course can be beneficial:

IT Managers
Data Scientists
Software Engineers
System Administrators
Network Engineers
Database Administrators
Cybersecurity Analysts

Prerequisites of the Artificial Intelligence (AI) for IT Professionals (AI4IT) Course

There are no formal prerequisites for this Artificial Intelligence (AI) for IT Professionals Course.

Artificial Intelligence (AI) for IT Professionals (AI4IT) Course Overview

Artificial Intelligence (AI) is revolutionising IT operations by automating routine tasks, improving system performance, and enhancing overall IT efficiency. AI technologies such as machine learning, natural language processing, and predictive analytics empower IT professionals to manage, optimise, and secure IT systems more effectively by leveraging data-driven insights and intelligent automation.

Proficiency in AI is crucial for IT Managers, Data Scientists, Software Engineers, and other IT professionals involved in system management and optimisation. Mastering AI tools and techniques enables these professionals to streamline IT workflows, foresee system failures, optimise resource allocation, and improve security measures.

This intensive 1-day course equips delegates with fundamental concepts and practical skills in applying AI to IT operations. Through hands-on workshops and expert-led sessions, delegates gain a comprehensive understanding of AI applications in IT, including system automation, predictive maintenance, and intelligent security systems.

Course Objectives

To understand the foundational principles of AI and its relevance to IT operations
To explore various AI tools and techniques used in automating IT tasks
To apply machine learning algorithms for predictive system analytics
To analyse and interpret AI-generated insights for improved system management
To optimise IT workflows and resource management using AI technologies
To comprehend ethical considerations in deploying AI solutions in IT

Upon completing this course, delegates will have acquired the knowledge and skills necessary to implement and optimise AI-driven IT practices, making them invaluable assets in their professional fields.

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What’s included in this Artificial Intelligence (AI) for IT Professionals (AI4IT) Course?

World-Class Training Sessions from Experienced Instructors.
Artificial Intelligence (AI) for IT Professionals (AI4IT) Certificate.
Digital Delegate Pack.

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Duration

Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Neural Networks with Deep Learning Training Course Outline

Module 1: Introduction to Neural Networks

Introduction to Neural Networks
Supervised Learning with Neural Networks

Module 2: Neural Networks Fundamentals

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

Module 3: Shallow Neural Networks

Representation of a Neural Networks
Computing the Output of a Neural Network
Vectorised Implementation
- Feed Forward
- Back Propagation
Hidden Layer
Activation Functions
Gradient Descent for Neural Networks

Module 4: Deep Neural Networks

Deep L-Layer Neural Network
Forward Propagation
Computational Graphs
Backward Propagation
Neural Networks Training
Deep Representations
Building Blocks
Difference Between Parameters and Hyperparameters

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Who should attend this Neural Networks with Deep Learning Training Course?

The Neural Networks with Deep Learning Course is designed for individuals working in data science, artificial intelligence, and related fields who wish to deepen their understanding and practical skills in deep learning. The following are some professionals for whom this course can be beneficial:

Data Scientists
AI Engineers
Software Developers
Machine Learning Engineers
Researchers
Analysts
IT Professionals

Prerequisites of the Neural Networks with Deep Learning Training Course

There are no formal prerequisites for this Neural Networks with Deep Learning Course. However, a basic understanding of programming and familiarity with machine learning concepts would be beneficial.

Neural Networks with Deep Learning Training Course Overview

Proficiency in Deep Learning is crucial for Data Scientists, AI Engineers, Software Developers, and Researchers. Mastering this field empowers professionals to create innovative image and speech recognition solutions, natural language processing, autonomous vehicles, and predictive analytics. It is essential for those aiming to stay competitive and drive technological advancements across various industries.

Course Objectives

To understand the foundational principles of neural networks
To explore various deep learning architectures, including CNNs and RNNs
To apply Deep Learning algorithms in image and speech recognition tasks
To analyse and interpret profound learning model results effectively
To optimise and fine-tune neural networks for improved performance
To comprehend ethical considerations in deploying deep learning solutions

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What’s included in this Neural Networks with Deep Learning Training Course?

World-Class Training Sessions from Experienced Instructors
Neural Networks with Deep Learning Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Cognitive Computing Training Course Outline

Module 1: Introduction to Cognitive Computing

What is Cognitive Computing?
Features of Cognitive Computing Solution
Working of Cognitive Computing
Cognitive Computing Vs Artificial Intelligence
Advantages of Cognitive Computing

Module 2: Computational Linguistics

Syntax and Parsing
Semantic Representation
Semantic Interpretation
Making Sense of Text
Language Generation

Module 3: Cognitive Computing – Practical Applications

Knowledge Extraction and Summarisation
Sentiment Analysis
Virtual Worlds, Games, and Interactive Fiction
Natural Language User Interfaces
Other Applications

Module 4: Introduction to Machine Learning

What is Machine Learning?
Categories of Machine Learning
Scikit-learn Algorithm
Machine Learning Skills
Artificial Neural Networks
Implementation of Machine Learning

Module 5: TensorFlow for Implementing Deep Neural Networks

Installing TensorFlow
Convolutional Deep Neural Networks for Image Classification

Module 6: Tools and Techniques – Natural Language Processing

Overview of spaCy NLP Library
spaCy for
Assigning Part of Speech Tags
Dependency Parsing
Entity Recognition
Rule-Based Matching
Overview of OpenNLP Library and Installation

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Who should attend this Cognitive Computing Training Course?

The Cognitive Computing Course is tailored for individuals working in the Business Analysis field to help them analyse business operations thoroughly and help in strategy development. The following are some professionals for whom this course can be beneficial:

Business Analysts
Data Analysts
Project Managers
Product Managers
UX/UI Designers
Software Engineers
Operations Managers

Prerequisites of the Cognitive Computing Training Course

There are no formal prerequisites for this Cognitive Computing Course.

Cognitive Computing Training Course Overview

Cognitive Computing is revolutionising project management by automating routine tasks, improving decision-making, and enhancing overall project efficiency. Cognitive technologies such as machine learning, natural language processing, and predictive analytics empower project managers to plan, execute, and deliver projects more effectively by leveraging data-driven insights and intelligent automation.

Proficiency in Cognitive Computing is crucial for Project Managers, Programme Managers, PMO Directors, and other professionals involved in project delivery. Mastering Cognitive Computing tools and techniques enables these professionals to streamline project workflows, foresee risks, optimise resource allocation, and improve stakeholder communication. It is essential for those aiming to stay competitive and drive innovation in project management practices across various industries.

This intensive 1-day course equips delegates with fundamental concepts and practical skills in applying Cognitive Computing to Project Management. Through hands-on workshops and expert-led sessions, delegates gain a comprehensive understanding of Cognitive Computing applications in project management, including task automation, predictive analytics, and intelligent decision support systems.

Course Objectives

To understand the foundational principles of Cognitive Computing and its relevance to Project Management
To explore various Cognitive Computing tools and techniques used in automating Project Management tasks
To apply machine learning algorithms for predictive project analytics
To analyse and interpret Cognitive Computing-generated insights for improved decision-making
To optimise project workflows and resource management using Cognitive Computing technologies
To comprehend ethical considerations in deploying Cognitive Computing solutions in Project Management

Upon completing this course, delegates will have acquired the knowledge and skills necessary to implement and optimise Cognitive Computing-driven Project Management practices, making them invaluable assets in their professional fields.

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What’s included in this Cognitive Computing Training Course?

World-Class Training Sessions from Experienced Instructors
Cognitive Computing Certificate
Digital Delegate Pack

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Duration

Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Recommendation System Training Course Outline

Module 1: Introduction to Recommender Systems

What is Recommender System?
Types of Recommender Systems
How Recommender System Works?
Challenges of Recommender System

Module 2: Collaborative Recommendation Approaches

Memory Based Approaches
Model-Based Approach
Python Implementation

Module 3: Content Based Recommendation

What is Content-Based Recommendation System?
User Profile
Item Profile
Utility Matrix

Module 4: Hybrid Recommendation

Hybridisation Recommendation System
Monolithic Hybridisation Design
Parallelised Hybridisation Design
Pipeline Hybridisation Design

Module 5: Evaluating Recommender Systems

Evaluation Methods
- Experimental (Online Experiments)
- Non-Experimental (Offline Experiments)
- Simulation Experiments
Common Metrices

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Who should attend this Recommendation System Training Course?

The Recommendation System Course is tailored for individuals working in the data science and analytics field to help them understand and implement recommendation algorithms effectively. The following are some professionals for whom this course can be beneficial:

Data Scientists
Machine Learning Engineers
Data Analysts
Software Developers
Product Managers
Business Analysts
UX/UI Designers

Prerequisites of the Recommendation System Training Course

There are no formal prerequisites for this Recommendation System Course.

Recommendation System Training Course Overview

Recommendation systems are integral to many online platforms, providing personalised suggestions to users and enhancing their experience. By analysing user data and behaviour, recommendation systems help businesses increase engagement and drive sales through tailored content delivery.

Proficiency in building and optimising recommendation systems is crucial for Data Scientists, Machine Learning Engineers, and other professionals involved in data analysis and product development. Mastering recommendation algorithms and techniques enables these professionals to create systems that understand user preferences, predict user behaviour, and provide relevant recommendations.

This intensive 1-day course equips delegates with fundamental concepts and practical skills in developing recommendation systems. Through hands-on workshops and expert-led sessions, delegates gain a comprehensive understanding of collaborative filtering, content-based filtering, and hybrid recommendation approaches. Delegates learn to implement recommendation algorithms, evaluate their performance, and optimise them for diverse real-world applications.

Course Objectives

To understand the foundational principles of recommendation systems and their importance
To explore various recommendation techniques, including collaborative filtering and content-based filtering
To apply machine learning algorithms in developing recommendation systems
To analyse and interpret recommendation system outputs for improved user experience
To optimise recommendation algorithms for better accuracy and performance
To comprehend ethical considerations in deploying recommendation systems

Upon completing this course, delegates will have acquired the knowledge and skills necessary to implement and optimise recommendation systems, making them invaluable assets in their professional fields.

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What’s included in this Recommendation System Training Course?

World-Class Training Sessions from Experienced Instructors
Recommendation System Certificate
Digital Delegate Pack

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Duration

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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Who should attend this AI And ML With Excel Training Course?

The AI And ML With Excel Course is tailored for individuals working in data analysis and business intelligence fields to help them leverage AI and machine learning capabilities using Excel. The following are some professionals for whom this course can be beneficial:

Business Analysts
Data Analysts
Project Managers
Product Managers
Financial Analysts
Operations Managers
Excel Power Users

Prerequisites of the AI And ML With Excel Training Course

There are no formal prerequisites for this AI And ML With Excel Course.

AI And ML With Excel Training Course Overview

Artificial Intelligence (AI) and Machine Learning (ML) are transforming how data is analysed and utilised across various industries. Integrating these advanced technologies into Excel empowers professionals to perform complex data analysis, predictive modelling, and data-driven decision-making within a familiar tool.

Proficiency in AI and ML with Excel is crucial for Business Analysts, Data Analysts, Project Managers, and other professionals involved in data-intensive roles. Mastering these tools enables these professionals to uncover deeper insights, make accurate predictions, and drive strategic initiatives. It is essential for those aiming to stay competitive and enhance their data analysis capabilities.

This intensive 1-day course equips delegates with fundamental concepts and practical skills in applying AI and ML using Excel. Through hands-on workshops and expert-led sessions, delegates gain a comprehensive understanding of AI and ML applications in data analysis, including data preprocessing, model building, and result interpretation. Delegates learn to implement AI-driven tools, interpret ML insights, and integrate these solutions into their analytical processes effectively.

Course Objectives

To understand the foundational principles of AI and ML and their relevance to data analysis
To explore various AI and ML tools and techniques available in Excel
To apply machine learning algorithms for predictive analytics using Excel
To analyse and interpret AI-generated insights for improved decision-making
To optimise data workflows and resource management using AI technologies in Excel
To comprehend ethical considerations in deploying AI and ML solutions in data analysis

Upon completing this course, delegates will have acquired the knowledge and skills necessary to implement and optimise AI-driven data analysis practices using Excel, making them invaluable assets in their professional fields.

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What’s included in this AI and ML with Excel Training Course?

World-Class Training Sessions from Experienced Instructors
AI and ML with Excel Certificate
Digital Delegate Pack

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More information

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Duration

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 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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Who should attend this OpenAI Training Course?

The OpenAI Course is tailored for individuals working in the technology and data science fields to help them understand and implement OpenAI technologies effectively. The following are some professionals for whom this course can be beneficial:

Data Scientists
AI Engineers
Software Developers
Machine Learning Researchers
Business Analysts
IT Professionals
Product Managers

Prerequisites of the OpenAI Training Course

There are no formal prerequisites for this OpenAI Course.

OpenAI Training Course Overview

Artificial Intelligence (AI) is revolutionising numerous industries by automating complex tasks, improving decision-making, and enhancing overall efficiency. OpenAI technologies such as GPT-4 and DALL-E enable professionals to create intelligent applications, generate natural language responses, and produce high-quality content autonomously.

Proficiency in OpenAI is crucial for Data Scientists, AI Engineers, Software Developers, and other professionals involved in AI development. Mastering OpenAI tools and techniques empowers these professionals to build advanced AI models, generate insightful data analysis, and create innovative AI-driven applications. It is essential for those aiming to stay competitive and drive technological advancements in their respective fields.

This intensive 1-day course equips delegates with fundamental concepts and practical skills in applying OpenAI technologies. Through hands-on workshops and expert-led sessions, delegates gain a comprehensive understanding of OpenAI applications, including natural language processing, content generation, and AI model development. Delegates learn to implement OpenAI tools, interpret AI outputs, and integrate AI solutions into their workflows effectively.

Course Objectives

To understand the foundational principles of OpenAI and its relevance to various industries
To explore various OpenAI tools and techniques used in AI model development
To apply machine learning algorithms for natural language processing tasks
To analyse and interpret AI-generated content for improved decision-making
To optimise workflows and resource management using OpenAI technologies
To comprehend ethical considerations in deploying OpenAI solutions

Upon completing this course, delegates will have acquired the knowledge and skills necessary to implement and optimise AI-driven practices using OpenAI technologies, making them invaluable assets in their professional fields.

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What’s included in this OpenAI Training Course?

World-Class Training Sessions from Experienced Instructors
OpenAI Certificate
Digital Delegate Pack

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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 for Artificial Intelligence & Machine Learning

Our training experts have compiled a range of course packages on a variety of categories in Artificial Intelligence & Machine Learning, to boost your career. The packages consist of the best possible qualifications with Artificial Intelligence & Machine Learning, and allows you to purchase multiple courses at a discounted rate.

Artificial Intelligence & Machine Learning Package

Save upto 40%

3 courses

Included courses:

Introduction to AI Course£1995

Machine Learning Course£1995

Deep Learning Course£1995

Total without package: £5985

Package price: £3595 (Save £2390)

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Artificial Intelligence & Machine Learning FAQs

What is Artificial Intelligence and Machine Learning?

Artificial intelligence (AI) involves creating systems that mimic human intelligence, while Machine Learning (ML) is a subset of AI that enables machines to learn from data.

What are the benefits of obtaining an Artificial Intelligence and Machine Learning Certification?

Certifications in AI and ML validate expertise, enhance career prospects, increase earning potential, and provide practical skills relevant to various industries.

Are there any prerequisites for Attending Artificial Intelligence and Machine Learning Courses?

Please visit the course page of the desired course to find out the suitable prerequisite information.

Do you provide self-paced AI And Machine Learning Courses?

Yes, self-paced AI and Machine Learning Courses are available, allowing learners to study at their own convenience and pace.

What are the levels of difficulty for these Machine Learning & AI Courses?

Courses range from beginner to advanced levels, catering to different levels of prior knowledge and expertise in AI and Machine Learning.

What is the duration of the Artificial Intelligence and Machine Learning Course?

Course duration varies depending on the course you select to pursue.

Is prior coding knowledge required for this Artificial Intelligence Course Online?

Prior coding knowledge is beneficial but not always required, as some courses are designed for beginners and include introductory coding lessons.

What is included in these Artificial Intelligence and Machine Learning Courses?

In these courses, delegates will have training with our experienced instructors, a digital delegate pack consisting of important notes related to this course, and a certificate after course completion.

Who should attend this Machine Learning (ML) & Artificial Intelligence (AI) course?

These Artificial Intelligence and Machine Learning Courses are suitable for professionals, students, and enthusiasts interested in AI and ML, regardless of their current skill level or background.

What kind of jobs can I expect based on a Machine Learning and Artificial Intelligence Course?

Graduates can pursue roles such as data scientist, machine learning engineer, AI specialist, research scientist, and business intelligence analyst.

Do you provide corporate training for Management Training Courses?

Yes, corporate trainings are available for our Artificial Intelligence and Machine Learning Courses, tailored to the specific needs of organisations to enhance their teams' skills in management and AI.

Do you offer 24/7 support for the Machine Learning and Artificial Intelligence Course?

Yes, 24/7 support is provided for our Artificial Intelligence and Machine Learning Courses to ensure learners can access assistance whenever needed, enhancing their learning experience.

What are the best Artificial Intelligence & Machine Learning courses in the United Kingdom?

Please see our Artificial Intelligence & Machine Learning courses available in the United Kingdom

Which is the best training institute/provider of Artificial Intelligence & Machine Learning in the United Kingdom?

The Knowledge Academy is the Leading global training provider for Artificial Intelligence & Machine Learning.

What is the cost/training fees for Artificial Intelligence & Machine Learning in the United Kingdom

The training fees for Artificial Intelligence & Machine Learning in the United Kingdom starts from £1995.

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