Python Revolution: Machine Learning for Finance: An Introductory Guide to Machine Learnign In Finance
Reactive Publishing "Python Revolution: The Power of Data Science in Finance" is an indispensable resource for
156 73 7MB
English Pages 432 Year 2024
Table of contents :
Title Page
Contents
Chapter 1: Building Blocks of Financial Machine Learning
1.1 The Evolution of Quantitative Finance
1.2 Key Financial Concepts for Data Scientists
1.3 Statistical Foundations
1.4 Essentials of Machine Learning Algorithms
1.5 Data Management in Finance
Chapter 2: Emerging Innovations: Machine Learning Tools and Technologies in Finance
2.1 Computational Environments for Financial Analysis
2.2 Data Exploration and Visualization Tools
2.3 Feature Selection and Model Building
2.4 Machine Learning Frameworks and Libraries
2.5 Model Deployment and Monitoring
Chapter 3: Exploring Deep Learning: Applications in Financial Analysis.
3.1 Neural Networks and Finance
3.2 Convolutional Neural Networks (CNNs)
3.3 Recurrent Neural Networks (RNNs) and LSTMs
3.4 Reinforcement Learning for Trading
3.5 Generative Models and Anomaly Detection
Chapter 4: Predictive Analytics: Time Series Analysis and Forecasting in Finance.
4.1 Fundamental Time Series Concepts
4.2 Advanced Time Series Methods
4.3 Machine Learning for Time Series Data
4.4 Forecasting for Financial Decision Making
4.5 Evaluation and Validation of Forecasting Models
Additional Resources
Glossary of Terms
Title Page
Contents
Chapter 1: Building Blocks of Financial Machine Learning
1.1 The Evolution of Quantitative Finance
1.2 Key Financial Concepts for Data Scientists
1.3 Statistical Foundations
1.4 Essentials of Machine Learning Algorithms
1.5 Data Management in Finance
Chapter 2: Emerging Innovations: Machine Learning Tools and Technologies in Finance
2.1 Computational Environments for Financial Analysis
2.2 Data Exploration and Visualization Tools
2.3 Feature Selection and Model Building
2.4 Machine Learning Frameworks and Libraries
2.5 Model Deployment and Monitoring
Chapter 3: Exploring Deep Learning: Applications in Financial Analysis.
3.1 Neural Networks and Finance
3.2 Convolutional Neural Networks (CNNs)
3.3 Recurrent Neural Networks (RNNs) and LSTMs
3.4 Reinforcement Learning for Trading
3.5 Generative Models and Anomaly Detection
Chapter 4: Predictive Analytics: Time Series Analysis and Forecasting in Finance.
4.1 Fundamental Time Series Concepts
4.2 Advanced Time Series Methods
4.3 Machine Learning for Time Series Data
4.4 Forecasting for Financial Decision Making
4.5 Evaluation and Validation of Forecasting Models
Additional Resources
Glossary of Terms
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