Python is a great language for getting started with machine learning, as it is equipped with a number of useful libraries for data analysis (e.g., pandas) and fast prototyping (e.g., scikit-learn). Python not only allows beginners to develop machine learning projects with ease but also offers a rich framework for advanced users, thanks to a passionate open source community and the availability of libraries such as Theano and TensorFlow.
Charlotte Werger offers a hands-on overview of implementing machine learning with Python, providing practical experience while covering the most commonly used libraries, including NumPy, pandas, and scikit-learn. Charlotte demonstrates the power and flexibility of these libraries through examples and challenges that are inspired by real-life projects. In addition to direct experience with Python coding, you’ll gain advice on machine-learning best practices (for example, managing the bias-variance trade-off and rigorously cross-validating statistical models) and learn how to structure an end-to-end data-science project. Along the way, you’ll catch a glimpse of more advanced topics in AI.
Charlotte Werger is the ASI education manager at ASI Data Science. A data scientist with a background in econometrics, Charlotte has worked in finance as a quantitative researcher and portfolio manager for BlackRock and Man AHL, using data science to predict movements in stock markets. She is a former ASI fellow, where she worked on predicting staff performance from psychometric test results, and has also worked on energy smart meter data analysis. Charlotte holds a PhD in economics from the European University Institute and an MPhil from Toulouse School of Economics.
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