Professional Certificate in Applied AI Engineering

Course Overview

Artificial intelligence (AI) is revolutionising entire industries, changing the way companies across sectors leverage data to make decisions. To stay competitive, organisations need qualified AI engineers who use cutting-edge methods like machine learning algorithms and deep learning neural networks to provide data driven actionable intelligence for their businesses. This Professional Course is designed to equip you with the tools you need to succeed in your career as an Artificial Intelligence / Machine Learning / Deep Learning Engineer.

Learning Outcomes

• Develop skills that are transferrable across all industries: business, information technology, computer science, finance, retail, e-commerce, advertising, manufacturing, healthcare & others
• Begin your journey as an AI Engineering Specialist in fast-growing & in-demand industries today
• Become a technology professional experienced in applied AI engineering & analytics

Course Modules

The modules of this course are designed to give participants a broad and comprehensive knowledge of Applied AI Engineering. At the end of the course, participants will achieve the following learning objectives listed below for each module.

Introduction to Big Data, AI, ML, DL, and Python Programming Language

• Understand the meaning of Data Science, Big Data, Artificial Intelligence, Machine Learning and Deep Learning
• Understand the differences between Artificial Intelligence, Machine Learning and Deep Learning
• Understand the demand for Data Scientists in the current job market
• Use Python programming language for Data Science

Mastering Machine Learning with Python Programming Language

• Understand how Machine Learning can be used to solve the current issues of Big Data
• Understand the types of Machine Learning, and how to use them in the real world
• Gain new skills to add to your resume, such as regression, classification, clustering, sci-kit learn, and SciPy.
• Work on new projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more

Deep Learning, Neural Networks, & Keras

• Learn what a neural network is, what a deep learning model is, and the differences between them
• Gain an understanding of unsupervised deep learning models, such as auto-encoders and restricted Boltzmann machines
• Gain an understanding of supervised deep learning models, such as convolutional neural networks and recurrent networks
• Build deep learning models and networks using the Keras library

Computer Vision and Image Processing

• Understand the meaning of computer vision, and its application in different industries
• Learn about image processing and utilise IT libraries like Pillow and OpenCV
• Understand how image classification works
• Use project-based learning to create your own computer vision web app, and deploy it to the Cloud

Deep Neural Network with PyTorch

• Understand how PyTorch library works for Deep Learning
• Be able to explain and apply the knowledge of Deep Neural Networks, and related machine learning methods
• Use PyTorch library for Deep Learning applications
• Build Deep Neural Networks using PyTorch

Deep Learning with TensorFlow

• Explore the foundational TensorFlow concepts, such as the main functions, operations, and execution pipelines
• Learn how TensorFlow can be used in curve fitting, regression, classification, and minimization of error functions
• Understand the different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks, and Auto-encoders
• Apply TensorFlow for back-propagation to tune weights and biases, while Neural Networks are being trained

Projects with Deep Learning and AI

• Determine the most suitable kind of deep learning method to use in different situations
• Gain practical knowledge on how to build a deep learning model to solve real problems
• Master the process of creating a deep learning pipeline
• Apply knowledge of deep learning to improve models using real data
• Obtain the skills to present and communicate outcomes of deep learning projects

Data Scientist Marketing

• Understand how platforms like LinkedIn work, and how to build one professionally
• Build a competitor portfolio for the projects of Deep Learning
• Build a professional resume for job applications
• Understand the best techniques to improve and update your LinkedIn profile regularly