Artificial Intelligence & Machine Learning

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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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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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

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

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

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What’s included in this Deep Learning 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)

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

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

Course Objectives

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

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

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What’s included in this 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)

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 Business Managers Course?

The Artificial Intelligence (AI) for Business Managers 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 Business Managers Course

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

Artificial Intelligence (AI) for Project Managers Course Overview

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

 

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

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

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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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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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Prerequisites

There are no prerequisites for this course.

Who should attend?

This course is designed for Business Analysts.

Artificial Intelligence (AI) for Business Analysts Course Overview

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

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

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

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What's included?

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

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Online Instructor-led (1 days)

Classroom (1 days)

Online Self-paced (8 hours)

Artificial Intelligence (AI) for DevOps Course Outline

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 Course is designed to help DevOps professionals for understanding how to incorporate AI into their workflow for improved automation, analytics, operational efficiency, and other areas. The following are some professionals who will benefit significantly with this course:

  • DevOps Engineers
  • System Administrators
  • Security Analysts
  • Software Developers
  • Data Scientists
  • QA Engineers
  • IT Managers

Prerequisites of the Artificial Intelligence (AI) for DevOps Course

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

Artificial Intelligence (AI) for DevOps Course Overview

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

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

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

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What’s included 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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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 aimed at educating the Information Technology (IT) professionals about the increasing role and importance of Artificial Intelligence (AI) in IT. This course provides practical insights into AI technologies like Machine Learning, Natural Language Processing (NLP) and Data Analytics. The following are some of the professions that can benefit from this course:

  • IT Managers.
  • System Administrators.
  • Cybersecurity Analysts.
  • Network Engineers.
  • Cloud Architects.
  • Database Administrators.
  • Helpdesk and Support Staff.

Artificial Intelligence (AI) for IT Professionals (AI4IT) Prerequisites.

There are no formal prerequisites for this Artificial Intelligence (AI) for IT Professionals (AI4IT) Course. However, basic knowledge of statistics and programming would be beneficial.

Artificial Intelligence (AI) for IT Professionals Course Overview.

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

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

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

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What’s included 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)

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 will provide you a deep understanding of Neural Network Architectures and how they can be leveraged in various applications and industries through Deep Learning techniques. The following are some professionals who can greatly benefit from this course:

  • Data Scientists
  • Machine Learning Engineers
  • Software Developers
  • Data Engineers
  • DevOps Engineers
  • UI/UX Designers
  • Regulatory and Compliance Professionals

Prerequisites of the Neural Networks with Deep Learning Training Course

There are no formal prerequisites for this Neural Networks with Deep Learning Training, but a basic understanding of the Python programming language would be helpful.

Neural Networks with Deep Learning Training Course Overview

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

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

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

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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)

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 aimed at helping individuals working in the field of Cognitive Computing, which is an area of Computer Science that focuses on creating systems that can perform tasks requiring human-like intelligence. The following are some professionals that can benefit vastly from this course:

  • Artificial Intelligence (AI)/Machine Learning (ML) Engineers
  • Data Scientists
  • Software Developers
  • Business Analysts
  • IT Managers
  • UI/UX Designers
  • System Architects

Prerequisites of the Cognitive Computing Training Course

There are no formal prerequisites for this Cognitive Computing Course. 

Cognitive Computing Training​ Course Overview

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

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

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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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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 a specialised course that focuses on training professionals and enthusiasts in designing, implementing, and optimising Recommendation Systems. The following professionals will benefit greatly from this course:

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

Prerequisites of the Recommendation System Training Course

There are no formal prerequisites for this Recommendation System Course. 

Recommendation System Training​ Course Overview

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

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

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

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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)

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 Training Course is designed for professionals and analysts willing to learn how to leverage Artificial Intelligence (AI) and Machine Learning (ML) techniques using Microsoft Excel as a tool. This course is beneficial for various professionals including:

  • Business Analysts
  • Data Analysts
  • Operations Analysts
  • Supply Chain Analysts
  • IT Managers
  • Marketing Analysts
  • Financial Analysts

Prerequisites for the AI and ML with Excel Training Course

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

AI and ML with Excel Training Course Overview

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

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

Course Objectives

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

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

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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)

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 Training Course is designed for a wide range of professionals, researchers, developers, and individuals interested in learning about OpenAI's technologies and applications. This course is beneficial for various professionals including:

  • AI Developers
  • Machine Learning Engineers
  • Data Scientists
  • Natural Language Processing (NLP) Experts
  • Software Engineers
  • AI Researchers
  • Product Managers
  • Content Creators

Prerequisites for the OpenAI Training Course

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

OpenAI Training Course Overview

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

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

Course Objectives

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

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

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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 +61 272026926 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.

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Artificial Intelligence & Machine Learning FAQs

This depends on the training course you choose. The introduction to Artificial Intelligence and Deep Learning Training with TensorFlow takes 1 day, and the Machine Learning Training Course and NLP fundamentals with Python Essential Training lasts 2 days.
We offer Artificial Intelligence and Machine Learning training courses in locations all over the UK, as well as abroad. We make it easy to find a training venue near you!
The Knowledge Academy’s Artificial Intelligence and Machine Learning training courses include the courseware, a certificate, an experienced instructor and, refreshments.
Prerequisites vary according to the course. Please see each course for details.
The Knowledge Academy is the Leading global training provider for Artificial Intelligence & Machine Learning.
The training fees for Artificial Intelligence & Machine Learning in Australia starts from AUD3795.
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