Working with images can be a very time-consuming task, especially if you have many images to work on. Machine learning can thus be a great time-saver for various image analysis and editing tasks, such as finding the dominant colors of an image thanks to the K-means clustering algorithm.
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A key element of Artificial Intelligence, Natural Language Processing is the manipulation of textual data through a machine in order to “understand” it, that is to say, analyze it to obtain insights and/or generate new text. In Python, this is most commonly done with NLTK.
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Creating a stock trading bot is both a very interesting and a very challenging task. To build an algorithm that makes money, there is a number of potential trading strategies from which value can be created. This post attempts to list the most obvious strategies in a formal and systematic approach, to methodically structure the testing of the different ideas. This post will also be updated over time as more strategies get added and the most promising ideas are tested.
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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.
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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: