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Module 1: What is Artificial Intelligence (AI)?
Module 2: Application Areas of AI
Module 3: Artificial Intelligence and Related Fields
Module 4: Foundation of AI – Machine Learning
Module 5: Agents and Environments
Module 6: Concept of Rationality
Module 7: Fuzzy Logic Systems
Module 8: Overview of Robotics
Module 9: Natural Language Processing
Module 10: Neural Networks
In this Introduction to Artificial Intelligence Training course, there are no formal prerequisites.
This Introduction to Artificial Intelligence Training course is suitable for anyone who is willing to get in-depth knowledge of Artificial Intelligence. However, this course will be more beneficial for Technical and Non-Technical Professionals out there.
Artificial Intelligence (AI) is a group of methodologies in which the core algorithms are implemented in power structures such as fuzzy logic systems, artificial neural networks, colony optimisation, genetic algorithm, particle swarm optimisation, simulated annealing, and evolutionary computing. It is accomplished by analysing how the human brain functions while solving problems and using these outcomes to develop intelligent software and systems. AI is a field that is actively and continuously growing and changing. Attending this training course will help individuals to enhance the skills required to become successful AI professionals. This course will allow individuals to master AI applications, machine learning, natural language processing needed to excel in this domain and kick-start their career in Artificial Intelligence.
In this 1-day Introduction to Artificial Intelligence Training course, delegates will get to know about various functions, features, and uses of Artificial Intelligence. They will learn about different concepts such as machine learning in ANN's, artificial neural networks, Natural Language Processing (NLP), types of agents, agent's terminology, and more. They will understand the crucial role of Artificial Intelligence in various fields like healthcare, business, education, finance, law, and manufacturing. Delegates will also learn about the strength and limitations of machine learning-based AI, machine learning methods, and supervised, unsupervised, and semi-supervised machine learning algorithms, which will help them enhance their skills of working with AI.
This course includes various essential concepts as following:
At the end of this Introduction to Artificial Intelligence Training course, delegates will be able to able to work with fuzzy logic systems and machine learning tools. They can automate grading in the education sector with Artificial Intelligence's help and provide additional support to students with AI tutors. They may also be able to use AI automation within business sectors for repeatable tasks that humans usually handle.
After completing this Introduction to Artificial Intelligence training course, delegates can choose from The Knowledge Academy's wide range of courses from Artificial Intelligence & Machine Learning topic for enhancing their skills in the advancing world of AI.
This training course is beneficial for the delegates who wish to get proficient in machine learning. Professionals who are best suited for this course:
It is recommended that the delegates should possess some basic knowledge of python programming, statistics fundamentals, and high school mathematics for this course.
Machine learning is an application of artificial intelligence (AI). It is typically concerned with the computer programs that enable computers to evolve behaviours based on empirical data. Today, Machine Learning becomes a key technique to solve various problems in multiple areas such as computational finance and biology, NLP, and energy production.
This Machine learning training course will assist the delegate to gain mastery over machine learning. This training course guides the delegates through the different concepts of machine learning such as neural networks, algorithms, clustering, supervised and unsupervised learning. By the completion of the training course, the delegate will gain expertise in creating algorithms and applications in machine learning.
Machine Learning is one of the leading career choices these days. The demand for machine learning is increasing day by day, so in the upcoming years, the companies are expected to increase their investments in this technology.
This training course is designed for engineers who wish to get expertise in using the TensorFlow. It is recommended for the following professionals:
Basic knowledge about Python programming and machine learning can help the delegate to learn this course easily.
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.
Anyone who wishes to gain knowledge regarding Natural Language Processing can attend this course. This course is ideal for:
Basic knowledge of Python is recommended.
Natural Language Processing (NLP) is a powerful skill that helps you extract essential information from text data. The NLP Fundamentals with Python Training course guides delegates how to auto-summarise the text by using machine learning. Delegates will also acquire knowledge about how to build the text classifier using the Naive Bayes algorithm.
During this course, delegated will learn how to use the Natural Language Toolkit (NLTK) to pre-process raw text. You will master how to use NLTK with other Python Libraries such as SciPy, matplotlib, NumPy, and pandas.
There are no prerequisites for this course.
This course is well suited for:
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.
Results – Surveys
Results – Interviews
Analysis and Discussion
There are no prerequisites for this course, anyone with an interest in AI or the way that AI can support project management should attend.
This course is designed for Project Managers with an interest in developing their awareness of AI and how AI can support their projects, operations, and efficient management
Artificial Intelligence (AI) 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.
Introduction to Artificial Intelligence (AI)
Use of Artificial Intelligence (AI)
Artificial Intelligence (AI) and its Relevance to Banking
Artificial Intelligence (AI) Applications in the Banking Industry
Impact of Artificial Intelligence on Investing
AI and its Impact on Finance Industry
Future Evolution of Business Analyst
Hybrid Roles for Future Business Analyst
How Artificial Intelligence (AI) Change the Face of Business?
Co-Existence of Artificial Intelligence and Business Analysis
There are no prerequisites for this course.
This course is designed for Business Analysts.
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.
Introduction to Artificial Intelligence (AI)
Building Blocks of AI
AI vs. Machine Learning vs. Deep Learning
How to train AI?
Implementing AI in an Organisation
AI Use Cases in Information Management
(Human) Supervision of AI
Implementation Areas of AI
There are no prerequisites for attending this course. However, basic knowledge of statistics and programming will be beneficial.
This course is designed for IT Professionals.
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.
Machine Learning Basics
Introduction to Deep Learning
Artificial Neural Networks
Deep Neural Networks
Monte Carlo Methods
Deep Generative Model
Libraries and Frameworks
It is recommended that delegates possess some basic knowledge of Python, linear algebra and probability.
This course is intended for individuals who wish to learn how deep learning works and who may wish to develop their own applications using deep learning techniques.
Deep Learning is used for building and training neural networks – layers of decision-making nodes inspired by the human brain.
It is a subset of machine learning (ML) where artificial neural networks learn from large amounts of data. It allows machines to solve complicated problems even when using diverse, unstructured, and interconnected data sets.
This Deep Learning Training course will provide you with a basic understanding of the linear algebra, probabilities, and algorithms used in deep neural networks. After learning the difference between deep learning and machine learning, delegates will gain in-depth knowledge of the different types of neural networks such as feedforward, convolutional, and recursive. At the end of this course, delegates will be able to build complex models that help machines to solve real-world problems.
Introduction to Deep Learning
Neural Networks Fundamentals
Shallow Neural Networks
Deep Neural Networks
There are no prerequisites for this course. However, some experience with the Python programming language would be beneficial.
Anyone who wants to learn about neural networks and deep learning can attend this course.
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.
Introduction to Cognitive Computing
Basics of Linguistics
Cognitive Computing – Practical Applications
Introduction to Machine Learning
TensorFlow for Implementing Deep Neural Networks
Tools and Techniques – Natural Language Processing
There are no prerequisites for attending this course.
Anyone interested in learning how cognitive computing works with machine learning, deep neural networks, and NLP can attend this course.
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.
Introduction to Recommender Systems
Hybrid Recommendation Approaches
Recommender Systems Explanations
Evaluating Recommender Systems
There are no prerequisites to attend this course.
Anyone wishes to have a comprehensive knowledge of Recommendation system can attend this course. This course is well-suited for:
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.
Speak to a training expert for advice if you are unsure of what course is right for you. Give us a call on + 1-613 800 4703 or Inquire.
Our training experts have compiled a range of course packages to compliment a variety of categories in order to help fast track your career. The packages consist of the best possible qualifications in each industry and allows you to purchase multiple courses at a discounted rate.
Introduction to Artificial Intelligence TrainingCAD3995
Machine Learning TrainingCAD3995
Deep Learning CourseCAD3995
Total without package: CAD11985
Package price: CAD7195 (Save CAD4790)
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Course was run very smoothly, Richard our trainer was extremely knowledgeable and delivered the course in a succinct fashion with a twist of humour thrown in.
The course was great and the so was the trainer - brilliantly delivered and I would certainly recommend this course to my colleagues. Thanks to Richard for a great course and delivering it a tough environment virtually.
Richard was very knowledgeable and explained well
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"Really good course and well organised. Trainer was great with a sense of humour - his experience allowed a free flowing course, structured to help you gain as much information & relevant experience whilst helping prepare you for the exam"
Joshua Davies, Thames Water