Artificial Intelligence & Machine Learning

Online Instructor-led (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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Online Instructor-led (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 Deep Learning Training 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 Deep Learning Training, 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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Online Instructor-led (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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Online Instructor-led (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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Online Instructor-led (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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Online Instructor-led (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 the Introduction to Artificial Intelligence Course.

Introduction to Artificial Intelligence Course Overview

Artificial Intelligence (AI) stands at the forefront of technological evolution, shaping our future in unprecedented ways. This Artificial Intelligence Course delves into the intricate world of AI and Machine Learning, offering a comprehensive understanding of its mechanisms and impacts. As a rapidly advancing field, AI’s relevance spans various sectors, making it a pivotal area of study and application.

Understanding AI is crucial, particularly for professionals in tech, business, and innovation-driven fields. The Artificial Intelligence Course is designed not just for tech enthusiasts but also for strategists and decision-makers aiming to integrate AI into their operations. Mastery of AI principles is increasingly becoming a necessity, rather than an option, in the modern digital landscape.

The Knowledge Academy's 1-day AI Training provides a concise yet profound exploration of AI. Delegates will gain insights into AI's foundational concepts and real-world applications. This Artificial Intelligence Course is a gateway to harnessing the power of AI, equipping participants with the knowledge to drive innovation and efficiency in their respective domains.

Course Objectives

  • To provide an understanding of Artificial Intelligence and Machine Learning concepts
  • To equip delegates with the skills to identify AI opportunities in their professional fields
  • To enhance problem-solving capabilities using AI-driven approaches
  • To foster an understanding of ethical considerations and challenges in AI applications
  • To offer insights into the latest AI trends and future prospects
  • To develop a strategic mindset for integrating AI into business solutions

Upon completing this Artificial Intelligence Course, delegates will emerge with a robust understanding of AI, ready to apply their knowledge in practical scenarios. This empowerment will enable them to be innovators and leaders in the AI space, driving positive change and advancement in their respective fields.

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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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Online Instructor-led (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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Online Instructor-led (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

Show moredown

Online Instructor-led (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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Online Instructor-led (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

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 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

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 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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Online Instructor-led (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?

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. This course will be beneficial for a wide range of professionals, including:

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

Prerequisites of the Cognitive Computing Training

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 by The Knowledge Academy 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?

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

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Online Instructor-led (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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Online Instructor-led (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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Online Instructor-led (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 +44 1344 203999 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.

Swipe for more. Don’t miss out!

Artificial Intelligence & Machine Learning FAQs

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.
Certifications in AI and ML validate expertise, enhance career prospects, increase earning potential, and provide practical skills relevant to various industries.
Please visit the course page of the desired course to find out the suitable prerequisite information.
Yes, self-paced AI and Machine Learning Courses are available, allowing learners to study at their own convenience and pace.
Courses range from beginner to advanced levels, catering to different levels of prior knowledge and expertise in AI and Machine Learning.
Course duration varies depending on the course you select to pursue.
Prior coding knowledge is beneficial but not always required, as some courses are designed for beginners and include introductory coding lessons.
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.
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.
Graduates can pursue roles such as data scientist, machine learning engineer, AI specialist, research scientist, and business intelligence analyst.
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.
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.
The Knowledge Academy is the Leading global training provider for Artificial Intelligence & Machine Learning.
The training fees for Artificial Intelligence & Machine Learning in Philippines starts from $2495
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