Share
Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques With Python (in English)
Kulkarni Akshay R" "Shivananda Adarsha" "Kulkarni Anoosh" "Krishnan V Adithya" (Author)
·
Apress
· Paperback
Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques With Python (in English) - Kulkarni Akshay R" "Shivananda Adarsha" "Kulkarni Anoosh" "Krishnan V Adithya"
$ 30.39
$ 37.99
You save: $ 7.60
Choose the list to add your product or create one New List
✓ Product added successfully to the Wishlist.
Go to My WishlistsIt will be shipped from our warehouse between
Monday, June 24 and
Tuesday, June 25.
You will receive it anywhere in United States between 1 and 3 business days after shipment.
Synopsis "Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques With Python (in English)"
This book teaches the practical implementation of various concepts for time series analysis and modeling with Python through problem-solution-style recipes, starting with data reading and preprocessing. It begins with the fundamentals of time series forecasting using statistical modeling methods like AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average), and ARIMA (autoregressive integrated moving-average). Next, you'll learn univariate and multivariate modeling using different open-sourced packages like Fbprohet, stats model, and sklearn. You'll also gain insight into classic machine learning-based regression models like randomForest, Xgboost, and LightGBM for forecasting problems. The book concludes by demonstrating the implementation of deep learning models (LSTMs and ANN) for time series forecasting. Each chapter includes several code examples and illustrations. After finishing this book, you will have a foundational understanding of various concepts relating to time series and its implementation in Python. What You Will LearnImplement various techniques in time series analysis using Python.Utilize statistical modeling methods such as AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average) and ARIMA (autoregressive integrated moving-average) for time series forecasting Understand univariate and multivariate modeling for time series forecastingForecast using machine learning and deep learning techniques such as GBM and LSTM (long short-term memory) Who This Book Is ForData Scientists, Machine Learning Engineers, and software developers interested in time series analysis.
- 0% (0)
- 0% (0)
- 0% (0)
- 0% (0)
- 0% (0)
All books in our catalog are Original.
The book is written in English.
The binding of this edition is Paperback.
✓ Producto agregado correctamente al carro, Ir a Pagar.
