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You will also build and evaluate neural networks, including RNNs and CNNs, using Keras and PyTorch to exploit unstructured data for sophisticated strategies.\u003c\/p\u003e\n\u003cp\u003eFinally, you will apply transfer learning to satellite images to predict economic activity and use reinforcement learning to build agents that learn to trade in the OpenAI Gym.\u003c\/p\u003e\n\u003ch4\u003eWhat you will learn\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eImplement machine learning techniques to solve investment and trading problems\u003c\/li\u003e\n\u003cli\u003eLeverage market, fundamental, and alternative data to research alpha factors\u003c\/li\u003e\n\u003cli\u003eDesign and fine-tune supervised, unsupervised, and reinforcement learning models\u003c\/li\u003e\n\u003cli\u003eOptimize portfolio risk and performance using pandas, NumPy, and scikit-learn\u003c\/li\u003e\n\u003cli\u003eIntegrate machine learning models into a live trading strategy on Quantopian\u003c\/li\u003e\n\u003cli\u003eEvaluate strategies using reliable backtesting methodologies for time series\u003c\/li\u003e\n\u003cli\u003eDesign and evaluate deep neural networks using Keras, PyTorch, and TensorFlow\u003c\/li\u003e\n\u003cli\u003eWork with reinforcement learning for trading strategies in the OpenAI Gym\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch4\u003eWho this book is for\u003c\/h4\u003e\n\u003cp\u003eHands-On Machine Learning for Algorithmic Trading is for data analysts, data scientists, and Python developers, as well as investment analysts and portfolio managers working within the finance and investment industry. 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