Artificial Intelligence Engineer (AI) - Master Programme - Ainutlaatuinen koulutus- ja sertifiointiohjelma - Tekoäly

Artificial Intelligence Engineer - Master's Program - A unique education!

Ai Learning & Certification Path - New updated 2024 version!

Elevate your professional journey by enrolling in this AI and ML program by AVC in collaboration with IBM. Acquire sought-after expertise in areas such as Machine Learning (ML), Deep Learning, Natural Language Processing (NLP), computer vision, reinforcement learning, generative AI, prompt engineering, ChatGPT, and many more. The program also includes hackathons and AMA sessions organized by IBM.

In partnership with IBM Add IBM benefits to your training program. Get access to exclusive Hackathons, Masterclasses and Ask-Me-Anything sessions from IBM.

What else do you get?

25 industry-relevant projects and integrated labs
You will have access to 25 industry-relevant projects. Hands-on applied learning with integrated labs.

Generative AI Edge
Live interactive sessions on the latest AI trends, such as ChatGPT, Generative AI, prompt engineering and more.

Immersive learning experience
8X higher interaction in live online classes delivered by experienced trainers and industry experts.

Learn from the best
LevelUp session by Andrew McAfee, Principal Research Scientist at MIT

YOU GET MORE THAN 12 COURSES!

Key features of the program

• Certificate of completion for AI Engineer program.
• Core courses delivered in live online classes by industry experts.
• 3 Capstones (final projects) and 25+ practical projects from different industry domains.
• Live interactive sessions on the latest AI trends, such as ChatGPT, generative AI, prompt engineering and much more.
• Exposure to TensorFlow, Keras, ChatGPT, OpenAI, Dall-E and other prominent tools.
• Masterclasses held by experts from IBM.
• Get IBM certificates for IBM courses.
• The very complete program will help you get noticed by the top recruiting companies.
• Exclusive hackathons and Ask Me Anything sessions by IBM.

What can you expect? Content/Topics:

• Basics of Generative AI, Prompt Engineering & ChatGPT
• Fundamentals of programming
• Python for Data Science by IBM
• Applied Data Science with Python
• Machine Learning
• Specialization on Deep Learning
• Basic course for AI engineers
• ADL and data vision
• Advanced Generative AI
• Deep Learning with Keras and TensorFlow by IBM
• Master Class for Industry - Artificial Intelligence
• Natural Language Processing (NLP)
• Reinforcement Learning

About the Masters in Artificial Intelligence

The Master's program in Artificial Intelligence, created in collaboration with IBM, introduces students to blended learning and prepares them to become specialists in artificial intelligence and data science. In Armonk, New York, IBM is a significant cognitive services and integrated cloud solutions company that provides many technology and consulting solutions. IBM invests $6 billion annually in research and development and has won five Nobel Prizes, nine U.S. National Medals of Technology, five U.S. National Medals of Science, six Turing Awards, and ten inductees into the U.S. Inventors Hall of Fame. IBM is a leader in AI and machine learning technologies for 2023. This AI Master's program will prepare students for careers in artificial intelligence and data analytics.

What will I get when I do this Masters program in Artificial Intelligence produced by AVC in collaboration with IBM?

You will receive certificates from IBM, AVC and Simplilearn upon completion of these courses. These certificates will attest to your abilities as an artificial intelligence expert. In addition, you will receive the following:

• Masterclass by IBM experts
• 'Ask me anything' sessions with IBM management.
• Hackathons conducted by IBM
• A certificate of completion that is internationally recognized by the industry.

What skills are covered by the Master's degree in Artificial Intelligence?

You will be able to demonstrate the following skills after completing this Master's in Artificial Intelligence:

• Gain insights into the latest AI trends such as generative AI, prompt engineering, ChatGPT and more.
• Learn how to use effective prompt engineering techniques to improve the performance and control the behavior of generative AI models.
• Master AI and ML comprehensively and understand their meaning, purpose, scope, stages, applications, and impact.
• Navigate data science intricacies with expertise, encompassing processes, wrestling, exploration, visualization, hypothesis building and testing.
• Perform scientific and engineering computations seamlessly using the SciPy suite, including sub-packages such as Integrate, Optimize, Statistics, IO, and Weave.
• Excel in mathematical calculations using the NumPy and scikit-learn packages.
• Gain expertise in supervised and unsupervised learning, recommendation engines, and time series modeling.
• Validate machine learning models effectively by decoding different accuracy metrics.
• Understand and apply deep learning in different applications.
• Navigate the layers of data abstraction in neural networks and gain unprecedented insights into data.
• Use tools like Keras to build computer vision applications.
• Become well versed in generative adversarial networks (GAN).
• Perform distributed and parallel computations efficiently using high-performance GPUs.
• Understanding of natural language and natural language generation
• Master natural language understanding and generation and immerse yourself in the basics of NLP using Python's Natural Language Toolkit (NLTK).
• Learn how to use machine learning and deep learning seamlessly with NLP.
• Convert text to speech with automated speech recognition.
• Learn how to apply reinforcement learning theory with Python and TensorFlow.
• Master different ways of solving reinforcement learning problems through different industry standard strategies.

What are the educational objectives of this Master's in Artificial Intelligence?

AVC and IBM partnered to create an Artificial Intelligence Master's program that combines artificial intelligence, data science, machine learning, and deep learning, enabling the application of sophisticated tools and models in the real world. This AI master's program will teach you the principles of machine learning statistics, python programming, data visualization, and feature development. The courses will teach you how to use Python libraries such as TensorFlow, Matplotlib, and Scikit-learn, as well as basic machine learning techniques such as supervised and unsupervised learning and advanced concepts such as artificial neural networks, data warehousing and feature extraction, and TensorFlow.

Artificial intelligence and machine learning will significantly impact all aspects of daily life in the near future, with applications in healthcare, aviation, finance, logistics and customer support. A job in artificial intelligence puts you on the fast track to a dynamic, ever-changing industry that is predicted to grow significantly and beyond by the time you graduate.

What projects are included in this Master's in Artificial Intelligence?

Over 15 real-world projects in various fields are included in this Master's in AI developed together with IBM. These projects are intended to help you understand key AI topics such as supervised and unsupervised learning, reinforcement learning, support vector machines, deep learning, TensorFlow, neural networks, convolutional neural networks, and recurrent neural networks.

The training includes a final assignment that will allow you to review the principles you learned. You will receive specialized, guided lessons to develop a high-quality industry project that addresses a real-world problem. This AI Masters Program final project will cover everything from exploratory data analysis to model construction and adaptation. You will use advanced AI-based supervised and unsupervised algorithms such as regression, Multinomial Naive Bayes, SVM, tree-based algorithms, and NLP in the topic of your choice to complete this final project. You will receive a certificate of completion of the project.

You will also have a real project experience, which you can present to future employers as a practical proof of your new knowledge from the course.

Project 1 - Socials

To reduce social hate and negativity, create a model that uses natural language processing and machine learning to recognize inappropriate tweets that should be removed from Twitter.

Project 2 - E-commerce

Amazon Prime Video movie reviews are included in the data collection. Analyze the dataset of movie reviews from Amazon's customers and create a recommendation system with machine learning that assigns scores to each user.

Project 3 - Vehicles

Mercedes wants to reduce the time spent in the test bench to reduce the time to bring a product (car) to market. Create and optimize a machine learning algorithm to solve this challenge.

Project 4 - Retail

Create a predictive model to forecast sales for Walmart stores taking into account special discount offers. Examine how sales are affected by macroeconomic variables such as the CPI and unemployment rates.

Project 5 - Telecommunications

Comcast intends to increase customer satisfaction by identifying and addressing problem areas and is looking for solutions that can be implemented.

Project 6 - E-commerce

ML engineers need to analyze Amazon users' evaluations of various items based on the provided data set and predict the sentiment or satisfaction based on the content of the features or reviews.

Project 7 - Commerce

The banking sector is the most common employer of data scientists. Fraudsters trying to cheat the system are constantly targeting it. Credit card companies must be able to detect illegal credit card fraud, despite the difficulty of accurately recognizing fraudulent and illegal activities. Multiple methods, such as overfitting classification, unsupervised detection methods and heuristics, must be used to achieve maximum accuracy in fraud detection.

Project 8 - Retail

The most important aspect of retail supply chain management is demand forecasting. To do this effectively, professionals must understand data science and ensemble methodology. For the upcoming month, you need to forecast the daily sales for each store.

Who should take this AI Masters?

AVC's Master's program in Artificial Intelligence is well suited for several professional fields and careers, such as

• Individuals interested in working as an AI or machine learning engineer.
• Analytical managers supervising a group of analysts.
• Data architects who want to learn about artificial intelligence systems and algorithms.
• Data analysts interested in working with machine learning or artificial intelligence.
• Professionals interested in artificial intelligence or machine learning as a profession.
• Experts who want to improve their understanding of their fields using artificial intelligence.

Artificial Intelligence Learning Path

Course 1: Introduction to Artificial Intelligence

This introduction to Artificial Intelligence (AI) is designed to help students decode the mystery of Artificial Intelligence and its business applications. This AI course for beginners provides an overview of AI concepts and workflows, machine learning, deep learning, and performance measurement.

Course content (e-learning)

This course includes the following modules:

• Introduction of the course
• Decoding artificial intelligence
• Basics of machine learning and deep learning
• Machine learning workflow
• Performance measurements

Learning objectives

At the end of this course you will be able to understand:

• The meaning and purpose of AI, as well as the scope, steps, applications and impacts
• The basic concepts of machine learning and deep learning
• How to effectively implement the steps of a machine learning workflow
• The difference between supervised, semi-supervised and unsupervised learning
• The role of performance metrics and how to identify key practices

Course 2: Fundamentals of Generative AI, Prompt Engineering and ChatGPT

This course explores generative AI models in depth, with particular emphasis on ChatGPT. Participants will understand the basics of generative AI and its scope, prompt engineering, explainable AI, conversational AI, ChatGPT, other major language models, and more.

Learning Objectives:

• Acquire a solid foundation in generative AI models, covering their core principles and different models.
• Understand the concept of explainable AI, grasp its meaning, and distinguish between different approaches to achieve explainability in AI systems.
• Use effective prompt engineering techniques to improve the performance and regulate the behavior of generative AI models.
• Develop a comprehensive understanding of ChatGPT, including its operational mechanisms, notable features and limitations.
• Explore a range of applications and scenarios where ChatGPT can be used effectively.
• Master fine-tuning techniques to adapt and optimize ChatGPT models.
• Recognize the ethical challenges of generative AI models to ensure responsible data use, mitigate bias, and prevent misuse.
• Understand the transformative potential of generative AI across industries and explore prominent tools.
• Gain insights on the future of generative AI, its challenges, and the necessary steps to unlock its full potential.

Topics covered:

• Generative AI and its Landscape
• Explainable AI
• Conversational AI
• Prompt Engineering
• Designing and Generating Effective Prompts
• Large Language Models
• ChatGPT and its Applications
• Fine-tuning ChatGPT
• Ethical Considerations in Generative AI Models
• Responsible Data Usage and Privacy
• The Future of Generative AI
• AI Technologies for Innovation

Course 3: Programming Refresher

In this course, you will gain basic Python skills that will serve as one of the building blocks for your journey throughout the program.

Learning Objectives:

• Gain knowledge of procedure-based and object-oriented programming.
• Understand the benefits of using benefits of using Python as a programming language.
• Install Python and its integrated development environment.
• Familiarize yourself with Jupyter Notebook and its use.
• Implement Python's identifiers, indents and comments effectively.
• Understand Python's data types, operators, and string functions.
• Learn about different types of loops in Python.
• Explore variable scopes within functions.
• Explain the concepts of object-oriented programming and its properties.
• Describe methods, attributes and access modifiers in Python.
• Gain an understanding of multithreading.

Topics covered:

• Fundamentals of programming
• Introduction to Python programming
• Data types and operators in Python
• Conditional statements and loops in Python
• Python functions
• Object-oriented programming
• Concepts with Python
• Threading/ Threading

Course 4: Python for Data Science (IBM)

This course has been designed by IBM and teaches students to use Python for data science. Upon completion of this course, you will be able to write Python scripts and perform critical hands-on data analysis using a Jupyter-based lab environment.

Learning Objectives:

• Use variables, strings, functions, loops, and conditions to create your first Python program.
• Gain an understanding of lists, sets, dictionaries, conditionals, branching, objects, and classes.
• Utilize pandas to load, manipulate, and save data, and read and write files in Python.

Topics covered:

• Basics of Python
• Data structures in Python
• Basic programming in Python
• Working with data in Python
• Working with NumPy arrays

Course 5: Applied Data Science with Python

This course provides a comprehensive understanding of basic data science, including data preparation, model building, and evaluation. Participants will learn concepts such as strings, Lambda functions, and lists. In addition, they will explore topics such as NumPy, linear algebra, and statistical concepts, including measures of central tendency and dispersion, skewness, covariance, and correlation. The course also covers hypothesis testing, such as Z-tests, T-tests and ANOVA, and data manipulation with pandas.
Participants will develop data visualization skills using popular libraries such as Matplotlib, Seaborn, Plotly, and Bokeh.

Industry project: Amazon & Walmart

Course Content (Online Classroom Flexi Pass)

Learning Objectives:

• Explain the basics of data science and its practical applications.
• Explore the processes of data preparation, model building and evaluation.
• Apply Python concepts such as strings and understand Lambda functions and functions and lists.
• Develop a solid understanding of the basics of NumPy.
• Explore indexing arrays and techniques.
• Apply principles of linear algebra in data analysis.
• Understand the application of calculus in linear algebra.
• Calculate measures of central tendency and dispersion.
• Gain a clear understanding of statistical concepts such as skewness, covariance and correlation.
• Describe the null hypothesis and the alternative hypothesis.
• Examine different hypothesis tests, including the Z-test and T-test.
• Understand the concept of ANOVA.
• Work with pandas' two primary data structures structures: Series and DataFrame.
• Use pandas for tasks such as data loading loading, indexing, reindexing, and merging.
• Prepare, format, normalize and standardize data using data binning techniques.
• Create visualizations using Matplotlib, Seaborn, Plotly and Bokeh.

Topics covered:

• Introduction to data science
• Basics of Python programming
• NumPy
• Linear algebra
• Basic statistics
• Probability distributions
• Advanced statistics
• Working with pandas
• Data analysis
• Processing data
• Visualization of data
• End-to-end statistics applications in Python

Course 6: Machine Learning

The course provides a comprehensive overview of different types of machine learning and their practical applications. You will explore the machine learning pipeline and gain insights into supervised learning, regression models, and classification algorithms. In addition, you will study unsupervised learning, clustering techniques, and ensemble modeling. Evaluate popular machine learning frameworks such as TensorFlow and Keras, and build a recommendation engine with PyTorch.

Learning Objectives:

• Examine the different types of machine learning and their respective characteristics.
• Analyze the machine learning pipeline and understand the main operations involved in machine learning operations (MLOps).
• Learn about supervised learning and its wide range of applications.
• Understand the concepts of overfitting and underfitting and use techniques to detect and prevent them.
• Analyze different regression models and their suitability for different scenarios.
• Identify linearity between variables and create correlation maps.
• List different types of classification algorithms and understand their
  specific applications.
• Master different types of unsupervised learning methods and approach them.
• Gain a deep understanding of different clustering techniques in
  unsupervised learning.
• Investigate different techniques for ensemble modeling techniques such as bagging, boosting, and stacking.
• Evaluate and compare different machine learning frameworks, including TensorFlow and Keras.
• Build a recommendation engine using PyTorch.

Topics covered:

• Machine Learning
• Supervised Learning
• Regression & Applications
• Classification & Applications
• Unsupervised Learning
• Ensemble Learning
• Recommendation Systems

Course 7: Deep Learning Specialization

This comprehensive course provides you with the knowledge and skills to effectively use deep learning tools with AI/ML frameworks. You will explore the fundamental concepts and practical applications of deep learning while gaining a clear understanding of the differences between deep learning and machine learning. The course covers a wide range of topics, including neural networks, forward and backward propagation, TensorFlow 2, Keras, performance optimization techniques, model interpretability, Convolutional Neural Networks (CNN), transfer learning, object detection, Recurrent Neural Networks (RNN), autoencoders, and neural network creation in PyTorch. By the end of the course, you will have a solid foundation in deep learning principles and the ability to build and optimize deep learning models efficiently using Keras and TensorFlow.

Achieve our Deep Learning certification and gain a competitive advantage over your peers in the next interview.

Powered By: TensorFlow

Learning Objectives:

• Differentiate between deep learning and machine learning and understand their respective applications.
• Gain a thorough understanding of different types of neural networks.
• Master the concepts of forward propagation and backward propagation in deep neural networks (DNN).
• Gain insight into modeling techniques and performance improvement in deep learning. learning.
• Understand the principles of hyperparameter tuning and model
  interpretability.
• Learn about key techniques such as dropout and early stopping and implement them effectively.
• Develop expertise in convolutional neural networks (CNN) and object detection.
• Acquire a solid understanding of recurrent neural networks (RNN).
• Become familiar with PyTorch and learn how to create neural networks using this framework.

Topics covered:

• Introduction to Deep Learning
• Artificial Neural Networks
• Deep Neural Networks
• TensorFlow
• Model Optimization and Performance Improvement
• Convolutional Neural Networks (CNNs)
• Transfer Learning
• Object Detection
• Recurrent Neural Networks (RNNs)
• Transformer Models for Natural Language Processing (NLP)
• Getting Started with Autoencoders
• PyTorch

Course 8: Ai final project

AVC's Ai Final Project gives you an opportunity to apply the skills you learned in the Artificial Intelligence course. With dedicated mentoring sessions, you will know how to solve a real-world, industry-appropriate problem. The project is the final step of the training and will help you showcase your expertise to employers.

Learning Outcomes:

The capstone/final project will increase your understanding of the artificial intelligence decision-making cycle, including performing exploratory data analysis, building and fine-tuning a model using advanced AI-based algorithms, and presenting results.

Course content

1. AI final project
2. Exploratory data analysis
3. Exploratory data analysis
4. Building and fitting models
5. Unsupervised learning
6. Representation of results

Master's Program Certificate you will receive:

* You will receive individual certificates for each course.

This training will give you the following extra bonus materials:

EXTRA'S - Elective Courses

1. Deep Learning with TensorFlow (IBM)
2. Advanced Deep Learning & Computer Vision
3. Natural Language Processing and Speech Recognition
4. Reinforcement Learning
5. Advanced Generative AI
6. Industry Master Class - Artificial Intelligence

You do not need to take these courses to get your Master Certificate. You have the option to take these courses as part of the overall program.

Elective courses

Deep Learning with Tensorflow (IBM)

This course takes your machine learning skills to the next level by providing a comprehensive understanding of Deep Learning with TensorFlow and Keras.
Become proficient in Deep Learning concepts, allowing you to construct artificial neural networks neural networks and navigate through layers of data abstraction. By unlocking the potential of data, this course prepares you for new frontiers in artificial intelligence.

Advanced Deep Learning and Computer Vision

This comprehensive course provides in-depth knowledge and practical skills in computer vision and advanced deep learning techniques. You will delve into various topics, including image formation and processing, Convolutional Neural Networks (CNN), object detection, image segmentation, generative
generative models, optical character recognition, distributed and parallel computing, and the use of deep learning models. At the end of the course, you will have the expertise to tackle complex computer vision challenges and successfully deploy deep learning models in various applications.

Natural Language Processing and Speech Recognition

In this course, you will gain a detailed understanding of the science behind the application of machine learning algorithms to process large amounts of natural language data. The course focuses on natural language understanding, feature engineering, natural language generation, automated speech recognition, speech-to-text conversion, text-to-speech conversion, and voice assistance
devices.

Reinforcement Learning

This course offers a comprehensive exploration of the core concepts of reinforcement learning. You will learn how to solve reinforcement learning problems using different strategies through practical examples and hands-on exercises using Python and TensorFlow. The course covers the theory behind RL algorithms and equips you with the skills to effectively use reinforcement learning as a problem-solving strategy. By the end of the course, you will be able to use RL algorithms to tackle a wide range of real-world challenges.

Advanced Generative AI

Dive deep into innovative generative AI principles with this advanced course. During the program, you will thoroughly explore neural networks, LLMs, their architectures, and various generative models such as VAEs, GANs, autoencoders, and transformer-based models. Immerse yourself in well-known generative AI models such as GPT, BERT and T5 and master the art of effectively assessing their performance. Participate in hands-on learning activities and gain practical expertise in building and deploying a conversational chatbot that engages in meaningful dialog interactions.

Career opportunities and job prospects for AI engineers

Are AI engineers in high demand? Yes, AI engineers are in high demand as the demand for AI technologies is increasing across various industries. According to Statistics Sweden, career growth for AI engineers is expected to reach at least 31.4% by 2030.

Career opportunities for AI engineers?
AI is the fastest growing field in the job market, with a projected expansion of 38.1% between 2022 and 2030. As AI becomes more prevalent in the hospitality, healthcare, financial, e-commerce and entertainment industries, there will be a high demand for AI engineers with the right skills and expertise.

AI has many career opportunities, such as machine learning engineer, data scientist, AI researcher, robotics engineer, AI consultant, computer vision engineer, natural language processing engineer and AI product manager.

Does AI have potential in the future?
Yes, AI has great opportunities in the future. The job market for AI engineers is expected to grow exponentially, revolutionizing healthcare, finance, retail, transportation, education, entertainment, and more industries.

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See for FAQ at the end of this description.

In short, what are these unique AVC Master Programs?

AVC's Masterprograms

Achieve your career goals with industry-recognized Learning Paths.

AVC's Master's programs are intensive, online bootcamps for: Data Science and Analytics, AI and Machine Learning, Big Data Cloud Computing, Cyber Security, Project Management, Full Stack Web Development, and Digital Marketing, among others.

These Learning Paths consist of different courses and topics that are related to specific skills for a role or job, e.g. Data Analyst, Cyber Security Specialist or Digital Transformation Leader.

These courses have been developed in collaboration with e.g. EY; IBM and Purdue University. Upon completion of the course, you will receive a recognized certificate from e.g. AVC, IBM and Purdue University, adding value to your profile.

Learning Parths consists of a number of online classroom and e-learning courses that you can take at your own time and pace. Very flexible. You have 11-12 months access to the Learning Path/program. Each program consists of approximately 8-9 courses and topics.

QUESTIONS AND ANSWERS

What is a Master in Artificial Intelligence?

A Master's in Artificial Intelligence is a comprehensive training program that helps students learn about this powerful technology from the ground up and develop work-ready AI skills.

Is the Masters in Artificial Intelligence worth it?

A Master's in Artificial Intelligence will help you gain a competitive advantage over your peers and build job-ready skills. The instruction is offered by top-notch industry experts who have rich domain experience. By enrolling in this AI master's program, you will clearly understand various AI concepts like machine learning, natural language processing, computer vision, deep learning, neural networks, etc.

Why enroll in the AI engineering course at AVC?

The Masters in Artificial Intelligence offered by AVC, in collaboration with IBM, is designed by industry experts to help you accelerate the growth of your career. It includes industry-relevant courses, such as Data Science with Python, Machine Learning, Deep Learning, NLP, and Chat GPT. It also has hackathons and AMA sessions hosted by IBM, Capstone projects, hands-on labs, live sessions, and practical projects.

Here are a few reasons why it is the best masters in artificial intelligence and machine learning:

Flexibility: The online master's in AI at AVC offers the flexibility to learn at your own pace and schedule.

Industry-relevant curriculum: AVC's online Master's in AI program provides a comprehensive curriculum covering key topics such as machine learning, deep learning, natural language processing, and data science.

Hands-on practical experience: The program emphasizes practical learning through real-world projects and case studies. This helps you gain valuable experience in applying AI techniques to solve real-world industry challenges and prepare you for the demands of the labor market.

Career advancement opportunities: Completing an online Master's in AI from AVC can enhance your career prospects in the rapidly growing field of artificial intelligence, as it provides networking opportunities with industry professionals.

Are online Masters in Artificial Intelligence difficult to learn?

The difficulty of learning an online Master's in Artificial Intelligence can vary depending on an individual's previous knowledge and experience in the field. AVCs offers an online Master's program in AI that is designed to be accessible to beginners and suitable for people with a basic understanding of the field.

What is the value of a Master Certificate?

AVC's Master's program will help you master in-demand skills at a faster pace and increase your marketability. Whatever your career goals, whether you are an entry-level professional or looking for skill development opportunities to change careers, AVC's Master's programs will help accelerate those goals. These certificates are in high demand.

Note: We are not a university and do not award university degrees. This Master's means that you have gone through the entire program and gained all the essential knowledge of the subject and thus fully mastered the subject. Each course/part of the program also results in a certificate.

What knowledge and experience is required for the Master's programs?

In general, no previous experience is required to participate in the program. The training starts at the introductory level and proceeds (step by step) to the expert level. However, it is always helpful if you have basic knowledge or experience of the subject. Read more about your specific course details.

This program is self-paced, so you can learn at your own pace. You will start with a practical e-learning module, followed by a series of online courses tailored to your needs where you have the freedom to choose the dates and times that best suit your schedule - and if you miss a session, you can easily reschedule it. Each session is recorded so that you can go through the material as many times as needed.

By the end of the program, you will have gained extensive knowledge and be able to demonstrate and apply it in a variety of practical tasks and projects.

How long does it take to complete the Master's program?

This is very individual. Some people go through the program quite quickly (about 2-3 months), others need more time. You have access to the program and eLearning for one year. If you spend 5-10 hours a week on the program, it will take you about 6 months to complete the program. Note: Some other Master programs take longer. This is an estimate.

What is the structure of the Master's programs? Do I have to come to a training center?

The majority of the programs are entirely based on distance learning. Most of them include intensive online bootcamps with eLearning that you can complete at your own pace. These Learning Paths consist of different courses and topics that are related to specific skills for a role or job. There are also online classroom sessions via our advanced professional distance learning system. There are a range of different time slots to choose from and we always record the sessions so you can listen to them if you miss something or want to review information. Someone is always on hand to help and support you if you have any questions about the skills you are learning.

When can I take Master's online courses?

The timing of the courses varies from group to group. You will have access to a dashboard with several time slots for each session or topic, so you can choose what works best for you. Sessions can be scheduled on weekday afternoons, weekend mornings or evenings, depending on participants' interest and trainer availability. If you miss a session, you can always catch up by watching the recordings, so you never miss any content.

When can I unlock my Master Certificate?

Once you have completed at least 85% of the course material, your certificate will be unlockable. This applies to all Master programs. One of the criteria for obtaining the Master Certificate is to participate in the live courses. However, exceptions can be made if you are not able to attend live, but watching the recordings is still required. Find out more about your specific course or email us for more information.