Artificial Intelligence Lab
One of the simplest supervised machine learning tools used in data science, linear regression permits to find the best-fitting line that correlates data points in a two-dimensional space. Defining this line then enables the prediction of where other data points could be located in the space, if they have the same characteristics as the original data set or if they stand out of it.
Diving deeper in data science and the actual coding of data processing functions and machine learning algorithms with Python, this series of tutorial gives us a great taste of what can be done in finance and stock trading. Through a hands-on approach, it guides us through the programming needed to retrieve, manipulate and visualize data, and, more importantly, to extract actionable insights.
Continue reading “Data Science and Machine Learning for Finance”
This series of articles dives deeper into the actual applications of Machine Learning that are currently in use in many current technological processes and devices.
Through these posts entitled “Machine Learning is Fun!”, Adam Geitgey guides us step by step through the concepts, data, algorithms, code, results and pitfalls of machine learning applications from image, face and speech recognition to language translation and more. It also gathers several different sources for more details on each application and its development.
This series is really dense with detailed code, but it is also explained very clearly, step by step, with detailed illustration. It notably covers the use of a Convolutional Neural Network (including Generative Adversarial Network) and Recurrent Neural Network, together with some of their most prominent applications in daily life. It is a real course not to be missed for any ML developer!
Here is the list of posts with direct links: