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Know the concepts of feature engineering, data visualization, and hyperparameter optimization
Design, build, and test supervised and unsupervised ML and DL models
Discover seasonality, trends, and market regimes, simulating a change in the market and investment strategy problems and predicting market direction and prices
Structure and optimize an investment portfolio with preeminent asset classes and measure the underlying risk
Know the concepts of feature engineering, data visualization, and hyperparameter optimization
Design, build, and test supervised and unsupervised ML and DL models
Discover seasonality, trends, and market regimes, simulating a change in the market and investment strategy problems and predicting market direction and prices
Structure and optimize an investment portfolio with preeminent asset classes and measure the underlying risk
Bridges the gap between finance and data science by presenting a systematic method for structuring, analyzing, and optimizing an investment portfolio and its underlying asset classes
Covers supervised and unsupervised machine learning (ML) models and deep learning (DL) models, including techniques of testing, validating, and optimizing model performance
Presents a diverse range of machine learning libraries (such as statsmodels, scikit-learn, Auto ARIMA, and FB Prophet) and covers the Keras DL framework plus the Pyfolio package for portfolio risk analysis and performance analysis
Erscheinungsjahr: | 2021 |
---|---|
Genre: | Importe, Informatik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Taschenbuch |
Inhalt: |
xviii
182 S. 53 s/w Illustr. 182 p. 53 illus. |
ISBN-13: | 9781484271094 |
ISBN-10: | 1484271092 |
Sprache: | Englisch |
Einband: | Kartoniert / Broschiert |
Autor: | Nokeri, Tshepo Chris |
Auflage: | 1st edition |
Hersteller: | APRESS |
Verantwortliche Person für die EU: | APress in Springer Science + Business Media, Heidelberger Platz 3, D-14197 Berlin, juergen.hartmann@springer.com |
Maße: | 235 x 155 x 12 mm |
Von/Mit: | Tshepo Chris Nokeri |
Erscheinungsdatum: | 27.05.2021 |
Gewicht: | 0,312 kg |
Bridges the gap between finance and data science by presenting a systematic method for structuring, analyzing, and optimizing an investment portfolio and its underlying asset classes
Covers supervised and unsupervised machine learning (ML) models and deep learning (DL) models, including techniques of testing, validating, and optimizing model performance
Presents a diverse range of machine learning libraries (such as statsmodels, scikit-learn, Auto ARIMA, and FB Prophet) and covers the Keras DL framework plus the Pyfolio package for portfolio risk analysis and performance analysis
Erscheinungsjahr: | 2021 |
---|---|
Genre: | Importe, Informatik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Taschenbuch |
Inhalt: |
xviii
182 S. 53 s/w Illustr. 182 p. 53 illus. |
ISBN-13: | 9781484271094 |
ISBN-10: | 1484271092 |
Sprache: | Englisch |
Einband: | Kartoniert / Broschiert |
Autor: | Nokeri, Tshepo Chris |
Auflage: | 1st edition |
Hersteller: | APRESS |
Verantwortliche Person für die EU: | APress in Springer Science + Business Media, Heidelberger Platz 3, D-14197 Berlin, juergen.hartmann@springer.com |
Maße: | 235 x 155 x 12 mm |
Von/Mit: | Tshepo Chris Nokeri |
Erscheinungsdatum: | 27.05.2021 |
Gewicht: | 0,312 kg |