Applying Artificial IntelligenCE in engineering

What’s the Difference Between Artificial Intelligence, Machine Learning and Deep Learning?
Visualize them as concentric circles with AI — the idea that came first — the largest, then machine learning — which blossomed later, and finally deep learning — which is driving today’s AI explosion — fitting inside both.

Top 15 Deep Learning Applications in 2021
A quick overview of the deep learning technology and explore the top deep learning uses and applications.

How Is Artificial Intelligence And Machine Learning Used In Engineering?
Discuss why engineers should adopt AI in engineering.

Artificial intelligence as auto companies’ new engine of value
The automotive industry has been talking about four disruptive and mutually reinforcing major trends — autonomous driving, connectivity, electrification, and shared mobility — AI is a key technology for all four of the trends.

Types of Machine Learning and Top 10 Algorithms Everyone Should Know

🎦 But what is a Neural Network? | Deep learning (video) 

🎦 Introduction to Deep Learning (video series)

Face Recognition for Beginners
For beginners who want to start their carrier in the field of Computer Vision or AI by learning about what is face recognition, its types, and how it works.

Your First Machine Learning Project in Python Step-By-Step
Complete your first machine learning project using Python.

An Example of Graph Convolutional Networks
Look under the hood of Graph Convolutional Networks (GCNs).

🎦 Graph Convolutional Networks (GCNs) made simple (video)
Introduces Graph Convolutional Networks and works through a Content Abuse example.

Graph Signal Processing for Machine Learning
Reviews a few important contributions made by GSP concepts and tools, such as graph filters and transforms, to the development of novel machine learning algorithms.

How to Evaluate Generative Adversarial Networks
Discover techniques for evaluating generative adversarial network models based on generated synthetic images.

Understanding deep Convolutional Neural Networks 👁 with a practical use-case in Tensorflow and Keras
Present a concrete application of deep learning on how to build a deep neural network that classifies images into their categories.

Introduction to Python
Python programming fundamentals, a mandatory module for those who are new to Python.

Data Science for Beginners
Introducing data science in layman terms aiming to create awareness of typical data science workflow in a data-driven project. 

Big Data Visualization with Python
Turning raw data into visualizable charts & reports.

Practical Data Mining with Python
Transform large data into useful information and ultimately, into knowledge. Finding interesting patterns in these datasets.

Practical Artificial Intelligence with Machine and Deep Learning
Overview of artificial intelligence, machine learning, and deep learning. Introductory module before diving into more extensive machine learning and deep learning. Deep learning is a subset of machine learning, while machine learning is a subset of artificial intelligence. Anyone who is new to AI should attend this course first.

Machine Learning with Python
Comprehensive coverage of supervised and unsupervised machine learning algorithms and applications. Machine Learning is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something.

Deep Learning Foundations and Applications
Comprehensive coverage of deep learning and its applications. Deep learning is one of the techniques for implementing machine learning. The core of deep learning is a neural network mimicking the human brain. Deep Learning has enabled many practical applications of machine learning and by extension the overall field of AI.

Reinforcement Learning Fundamentals and Applications
Cover the various aspects of reinforcement learning (RL), including fundamentals, state-of-the-art models, and algorithms, as well as some simulation libraries used to implement and test RL.

Introduction to Natural Language Processing (NLP)
Natural language processing (NLP) is the combination of AI and linguistics that allows us to talk to machines as if they were human. This course reviews the fundamentals of natural language processing (NLP), then dives into the common practices of text processing, and follows by building an intelligent model for automated decision making.