دانلود کتاب پیش‌بینی پیشرفته با پایتون -تسلط بر تکنیک‌های مدرن پیش‌بینی با ابزارهای یادگیری ماشین و ابری

Advanced Forecasting with Python covers all machine learning techniques relevant for forecasting problems, ranging from univariate and multivariate time series to supervised learning, state-of-the-art deep forecasting models like LSTMs, recurrent neural networks, and numerous cloud-based forecasting setups. Rather than focus on a specific set of models, this book presents an exhaustive overview of all techniques relevant to practitioners of forecasting. It begins by explaining the different categories of models that are relevant for forecasting in a high-level language. Next, it covers univariate and multivariate time series models, followed by advanced machine learning and deep learning models such as recurrent neural networks, LSTMs, and cloud platforms. It concludes with reflections on model selection, like benchmark scores vs. understandability of models vs. compute time, and automated retraining and updating of models. Each of the models presented in this book is covered in-depth, with an intuitive, simple explanation of the model, a mathematical transcription of this idea, and Python code that applies the model to an example dataset. This book is a great resource for those who want to add a competitive edge to their current forecasting skillset. The book is also adapted to those who wish to start working on forecasting tasks and are looking for an exhaustive book that allows them to start with traditional models and gradually move into more and more advanced models. You can follow along with the code using the GitHub repository that contains a Jupyter Notebook per chapter. You are encouraged to use Jupyter notebooks for following along, but you can also run the code in any other Python environment of your choice. An environment.yml is provided for each notebook, which can be imported through Anaconda or other environment tools.