Introduction à l'intelligence artificielle Diapositives de présentation PowerPoint

The process of Machine Learning involves the following steps: - Data Gathering - Data Cleaning - Selecting the Right Algorithms - Building the Model & Finalizing - Data Transformation into predictions

The limitations of Deep Learning are interpretability, statistical reasoning, and the amount of data. Deep learning algorithms require large amounts of data and computational power, making them expensive and time-consuming. These models can be difficult to interpret, making it hard to understand how they arrived at their conclusions.

AI is booming due to the availability of large amounts of data, advancements in computing power, and the development of more sophisticated algorithms. Additionally, AI has the potential to revolutionize industries and improve efficiency, leading to increased interest and investment in the field.

The different types of Machine Learning are: Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, Reinforcement Learning

AI (Artificial Intelligence) has the potential to automate certain tasks and replace some jobs that can be done more efficiently and accurately by machines. However, AI also has the potential to create new jobs and improve productivity, which can lead to economic growth and job creation. It is important to note that the impact of AI on jobs will vary by industry and job type, and it is up to individuals and businesses to adapt to these changes and ensure that the benefits of AI are shared equitably.