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A practical approach to timeseries forecasting using Python.
Have you ever wondered how weather predictions, population estimates, and even the lifespan of the universe are made? Discover the power of time series forecasting with state-of-the-art ML and DL models. The course begins with the fundamentals of time series analysis, including its characteristics,...
| Clasificación: | Libro Electrónico |
|---|---|
| Formato: | Electrónico Video |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
Packt Publishing,
[2023]
|
| Edición: | [First edition]. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 520 | |a Have you ever wondered how weather predictions, population estimates, and even the lifespan of the universe are made? Discover the power of time series forecasting with state-of-the-art ML and DL models. The course begins with the fundamentals of time series analysis, including its characteristics, applications in real-world scenarios, and practical examples. Then progress to exploring data analysis and visualization techniques for time series data, ranging from basic to advanced levels, using powerful libraries such as NumPy, Pandas, and Matplotlib. Python will be utilized to assess various aspects of your time series data, such as seasonality, trend, noise, autocorrelation, mean over time, correlation, and stationarity. Additionally, you will learn how to pre-process time series data for utilization in applied machine learning and recurrent neural network models, which will enable you to train, test, and assess your forecasted results. Finally, you will acquire an understanding of the applied ML models, including their performance evaluations and comparisons. In the RNNs module, you will be building GRU, LSTM, Stacked LSTM, BiLSTM, and Stacked BiLSTM models. By the end of this course, you will be able to understand time series forecasting and its parameters, evaluate the ML models, and evaluate the model and implement RNN models for time series forecasting. What You Will Learn Learn data analysis techniques and handle time series forecasting Implement data visualization techniques using Matplotlib Evaluate applied machine learning in time series forecasting Look at auto regression, ARIMA, Auto ARIMA, SARIMA, and SARIMAX Learn to model LSTM, Stacked LSTM, BiLSTM, and Stacked BiLSTM models Implement ML and RNN models with three state-of-the-art projects Audience No prior knowledge of DL, data analysis, or math is required. You will start from the basics and gradually build your knowledge of the subject. Only the basics of Python are required. This course is designed for both beginners with some programming experience and even those who know nothing about data analysis, ML, and RNNs. The course is suitable for individuals who want to advance their skills in ML and DL, master the relation of data science with time series analysis, implement time series parameters and evaluate their impact on it and implement ML algorithms for time series forecasting. About The Author AI Sciences: AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences. Their courses have successfully helped more than 100,000 students master AI and data science. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Time-series analysis |x Data processing. | |
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| 650 | 0 | |a Python (Computer program language) | |
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| 650 | 7 | |a Time-series analysis |x Computer programs. |2 fast |0 (OCoLC)fst01151191 | |
| 650 | 7 | |a Time-series analysis |x Data processing. |2 fast |0 (OCoLC)fst01151192 | |
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