As the industry has evolved, the perception of the meaning of AI has changed. In the modern day when people say AI they are primarily referring to large machine learning models, which is what will be discussed in this module. The concept of machine learning refers to an algorithm that can find patterns in input data and improves itself as it is trained more extensively. The key is that it is not told how to think. Some models work by mimicking the biological brain.
In some cases it is just a buzzword used to grab attention and sell products so it is crucial to understand the inner workings of this mysterious and often misunderstood technology. By understanding the key concepts you will have a better idea of what is possible and not be misled by hype. You will also be better equipped to take advantage of the power that AI has to offer.
In this module we will cover the understanding of key topics in machine learning. We will highlight the importance of high quality training data. Then we will cover hand picked examples of AI architectures since there are so many, including information about how they work and what to expect from them as strengths and weaknesses. Consequently we will look into how different AI models can be trained and the types of training, therefore blending the architecture and training data. Finally we will look at the current state of AI and comment about how to get the most out of the products available on the market.