Introducing our new Artificial Intelligence Engineer Learning Path

By ILX Team | 7 May 2023

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: