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Machine Learning in Finance Using Python

22 chapters · 1 free to read, 21 in the PDF

Unlock the Power of Machine Learning in Finance

Are you ready to learn about Machine Learning and take your career to the next level? Our ebook provides a complete guide to working on machine learning problems with Python and its powerful Scikit-learn library. From theoretical concepts to practical insights, we cover all the steps you need to create accurate, effective models in finance.

We start by showing you how to preprocess and explore your data, then dive into the key elements of a successful machine learning project, including balancing bias and variance, choosing the right features, and developing new features. Then, we explore popular supervised learning algorithms, such as Multiple Linear Regression, and introduce unsupervised learning techniques, like cluster analysis.

Master the Art of Neural Networks

Finally, we cover the basics of Neural Networks and their applications in finance. With our complete explanation and examples, you’ll understand how this advanced technology can help you work with complex relationships and financial time series.

5 Full Examples to Boost Your Skills

Don’t just read about Machine Learning - do it! Our ebook provides 5 full examples, complete with data and code, so you can see the concepts in action. Whether you’re just starting out or looking to take your skills to the next level, our ebook has everything you need to succeed.

Chapters

  1. 1Machine Learning with PythonRead free
  2. 2What is Machine Learning?In the PDF
  3. 3Data Preprocessing in Data Science and Machine LearningIn the PDF
  4. 4Feature Selection in Machine LearningIn the PDF
  5. 5Train-Test Datasets in Machine LearningIn the PDF
  6. 6Evaluate Model Performance - Loss FunctionIn the PDF
  7. 7Model Selection in Machine LearningIn the PDF
  8. 8Bias Variance Trade OffIn the PDF
  9. 9Supervised Learning ModelsIn the PDF
  10. 10Multiple Linear RegressionIn the PDF
  11. 11Logistic RegressionIn the PDF
  12. 12Logistic Regression in Python using scikit-learn PackageIn the PDF
  13. 13Decision Trees in Machine LearningIn the PDF
  14. 14Random Forest Algorithm in PythonIn the PDF
  15. 15Support Vector Machine Algorithm ExplainedIn the PDF
  16. 16Multivariate Linear Regression in Python with scikit-learn LibraryIn the PDF
  17. 17Classifier Model in Machine Learning Using PythonIn the PDF
  18. 18Cross Validation to Avoid Overfitting in Machine LearningIn the PDF
  19. 19K-Fold Cross Validation Example Using Python scikit-learnIn the PDF
  20. 20Unsupervised Learning ModelsIn the PDF
  21. 21K-Means Algorithm Python ExampleIn the PDF
  22. 22Neural Networks OverviewIn the PDF