{"product_id":"machine-learning-for-time-series-with-python-forecast-predict-and-detect-anomalies-with-state-of-the-art-machine-learning-methods-paperback","title":"Machine Learning for Time-Series with Python: Forecast, predict, and detect anomalies with state-of-the-art machine learning methods - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eBen Auffarth\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBecome proficient in deriving insights from time-series data and analyzing a model's performance\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eKey Features: \u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eExplore popular and modern machine learning methods including the latest online and deep learning algorithms\u003c\/li\u003e\n\u003cli\u003eLearn to increase the accuracy of your predictions by matching the right model with the right problem\u003c\/li\u003e\n\u003cli\u003eMaster time-series via real-world case studies on operations management, digital marketing, finance, and healthcare\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBook Description: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eMachine learning has emerged as a powerful tool to understand hidden complexities in time-series datasets, which frequently need to be analyzed in areas as diverse as healthcare, economics, digital marketing, and social sciences. These datasets are essential for forecasting and predicting outcomes or for detecting anomalies to support informed decision making.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eThis book covers Python basics for time-series and builds your understanding of traditional autoregressive models as well as modern non-parametric models. You will become confident with loading time-series datasets from any source, deep learning models like recurrent neural networks and causal convolutional network models, and gradient boosting with feature engineering.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eMachine Learning for Time-Series with Python explains the theory behind several useful models and guides you in matching the right model to the right problem. The book also includes real-world case studies covering weather, traffic, biking, and stock market data.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eBy the end of this book, you will be proficient in effectively analyzing time-series datasets with machine learning principles.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat You Will Learn: \u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eUnderstand the main classes of time-series and learn how to detect outliers and patterns\u003c\/li\u003e\n\u003cli\u003eChoose the right method to solve time-series problems\u003c\/li\u003e\n\u003cli\u003eCharacterize seasonal and correlation patterns through autocorrelation and statistical techniques\u003c\/li\u003e\n\u003cli\u003eGet to grips with time-series data visualization\u003c\/li\u003e\n\u003cli\u003eUnderstand classical time-series models like ARMA and ARIMA\u003c\/li\u003e\n\u003cli\u003eImplement deep learning models like Gaussian processes and transformers and state-of-the-art machine learning models\u003c\/li\u003e\n\u003cli\u003eBecome familiar with many libraries like prophet, xgboost, and TensorFlow\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWho this book is for: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eThis book is ideal for data analysts, data scientists, and Python developers who are looking to perform time-series analysis to effectively predict outcomes. Basic knowledge of the Python language is essential. Familiarity with statistics is desirable.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 370\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.77 x 9.25 x 7.5 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e October 29, 2021\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":52493821411635,"sku":"9781801819626","price":90.5,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0300\/5595\/6612\/files\/Rk5ldmU2T3BNTFdxS0lpVzFrekd3UT09.webp?v=1759964306","url":"https:\/\/www.vysn.com\/en-ca\/products\/machine-learning-for-time-series-with-python-forecast-predict-and-detect-anomalies-with-state-of-the-art-machine-learning-methods-paperback","provider":"VYSN","version":"1.0","type":"link"}