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Introducing our new Artificial Intelligence Engineer Learning Path

Artificial Intelligence (AI) has grown rapidly since the 1950s and the scope for its use is vast. As it can be used by many industries for facial recognition, emails, social media, online banking, analysing and searching databases, navigation, the list could go on.

But without AI Engineers, this wouldn't be possible, as they create AI systems in line with human needs and mission outcomes.

What will you learn on our AI Engineer Learning Path?

This learning path is structured in five steps, and then there are four electives – here’s an outline of each step:

Step 1 – Introduction to Artificial Intelligence
: this step will help learners understand how AI can be used in business applications. It will provide an overview of AI concepts and workflows, Machine Learning, Deep Learning, and performance metrics, and will teach learners the difference between supervised, semi-supervised and unsupervised learning. You’ll also learn about:

  • Meaning, purpose, scope, stages, applications, and effects of AI
  • Machine Learning workflow and how to implement the steps effectively
  • The role of performance metrics and how to identify their essential methods
  • And more…

Step 2 – Applied Data Science with Python:
this step will teach you the essential concepts of Python programming and gain in-depth knowledge in data analytics, Machine Learning, data visualization, web scraping, and natural language processing. By the end of this step, you’ll:

  • Gain an in-depth understanding of Data Science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing
  • Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
  • Gain expertise in Machine Learning using the Scikit-Learn package
  • And more…

Step 3 – Machine Learning:
Machine Learning is a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. And this step will teach you concepts and techniques, including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modelling to develop algorithms. On completion of this step, you’ll:

  • Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach
  • Acquire thorough knowledge of the statistical and heuristic aspects of Machine Learning
  • Comprehend the theoretical concepts and how they relate to the practical aspects of Machine Learning
  • And more…

Step 4 – Deep Learning with Keras and TensorFlow:
this step developed in collaboration with IBM will teach you the concepts of deep learning and TensorFlow to build artificial neural networks and traverse layers of data abstraction. At the end of this step you’ll:

  • Understand the difference between linear and non-linear regression
  • Comprehend convolutional neural networks and their applications
  • Learn how to filter with a restricted Boltzmann machine (RBM)
  • And more…

Step 5 – AI Capstone Project:
this step will enable you to apply your learned skills and solve a real industry-aligned problem. You’ll also learn AI-based supervised and unsupervised techniques including Regression, SVM, Tree-based algorithms, and NLP. This Capstone course will also be based on the AI decision cycle, and these are the three project milestones:

  • Exploratory Data Analysis
  • Model Building and fitting
  • Unsupervised learning

After all steps have been completed in line with the required criteria, learners will receive two ‘Certificates of Achievement’ from Simplilearn and IBM. As part of the learning path, learners will also have access to these electives:

  • Python for Data Science
  • Advanced Deep Learning and Computer Vision
  • Natural Language Processing (NLP)
  • AI Industry Master Class

How is the learning path delivered?

This AI Engineer Learning Path is delivered digitally using:

  • Online self-learning: consisting of videos and quizzes
  • Live interactive classes: via live virtual classrooms and live interaction and monitoring
  • Hands-on experience: through final assessment, project work and virtual labs

Why should individual employees and/or teams book this learning path?

Training in this learning path will enable learners to:

  • Advance your career or expand your career prospects in a growing field
  • Be a pioneer in new and emerging technology development
  • Increase organisational efficiency, profits and make better business decisions
  • And more…

Visit ILX.com to learn more and book training