Solve Real-World Problems in Finance & Investing

Machine Learning for Finance Professionals

Become one of the few in the Finance Industry that can write & use real patented algorithms to advise Fortune 500 companies on investment banking and capital markets decisions. By the end of the course, you will be trained to identify the ideal opportunities to apply this technology in your specific environment, so that you can immediately use your new skills to make an impact on your team.

Applied Machine Learning

is the ideal next step for those interested in furthering their foundational Python skills. This is an advanced course that utilizes concepts learned from the Python Fundamentals course and combines them with popular machine learning algorithms from the popular Scikit-Learn Machine Learning Package to solve real-world business problems. Although this course can be taken by any business professional, it is catered for those interested in furthering their career in the following specialties:

  • Investment Banking

  • Sales & Trading

  • Capital Markets

  • Asset Management

  • Treasury Management

  • Corporate Development

After Completing the Course

You will master cutting-edge skills that will enable you to deliver meaningful results in your professional environment:

  • Using the Scikit-Learn Machine Learning Package in Python
  • Advanced data cleaning, exploration, and visualization
  • Identifying opportunities in your workplace
  • Regression algorithms
  • Classification algorithms
  • Using ML to advise corporations on raising capital
  • Using ML to predict investor behavior
  • Identifying overfit models and selecting optimal algorithms
  • Splitting data into training and testing sets
  • Constructing model pipelines with hyperparameter tuning
  • Building and finalizing a machine learning classifier from start to finish
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    Video & Applied Learning

    With multiple exercises and real case studies

    This program will teach you how to use machine learning algorithms to solve two real case studies from investment banking and capital markets applications.


    Liquidity Regressor Model
    This model will help advise large corporations on raising capital. Corporations often ask for advice about how much liquidity they should maintain and how they stack up against peer companies. You will use 5 different regression algorithms to find the optimal solution based on a set of inputs.

    Investor Classifier Model
    This model is an altered version of a real machine learning algorithm used by top investment banks to predict investor behavior when raising capital for large corporate clients. You will learn to use 4 different classification algorithms to determine which investors to invite to participate in the deal and how much capital you should invite them to commit.

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    Course curriculum

      1. Welcome and Course Objectives

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      2. FAQs: Maximizing Your Success in this Course

        FREE PREVIEW

      1. Learning Objectives

      2. Download Exercise Notebook ML01

      3. 1a - The Machine Learning Process

      4. 1b - Matplotlib and Seaborn

      5. 1c - A Few Quick Notes

      6. 1d - Exercise

      7. 1e - Solution

      8. 1f - Countplot Function

      9. 1g - Exercise

      10. 1h - Solution

      11. 1i - Replace Function and Sparse Classes

      12. 1j - Exercise

      13. 1k - Solution

      14. 1l - Exercise

      15. 1m - Solution

      16. 1n - Spotting Outliers

      17. 1o - Exercise

      18. 1p - Solution

      19. 1q - Exercise

      20. 1r - Solution

      21. 1s - Exercise

      22. 1t - Solution

      23. 1u - Exercise

      24. 1v - Solution

      25. 1w - Exercise

      26. 1x - Solution

      27. 1y - NaN Object

      28. 1z - Exercise

      29. 1aa - Solution

      30. 1ab - Dropping Null Values

      31. 1ac - Exercise

      32. 1ad - Solution

      33. 1ae - Boxplots with Seaborn

      34. 1af - Exercise

      35. 1ag - Solution

      36. 1ah - Saving Your DataFrame

      37. 1ai - Review

      1. Learning Objectives

      2. 2a - What Are Regression Algorithms?

      3. 2b - Real Relationships and Overfitting

      4. 2c - Preventing Overfitting with Regularization

      5. 2d - Decision Trees and Ensemble Methods

      1. Learning Objectives

      2. Download Exercise Notebook ML03

      3. 3a - Case Study Overview

      4. 3b - Exercise

      5. 3c - Solution

      6. 3d - Metadata

      7. 3e - Exercise

      8. 3f - Solution

      9. 3g - Splitting Your Data

      10. 3h - Exercise

      11. 3i - Solution

      12. 3j - train_test_split() Function

      13. 3k - Unpacking Lists

      14. 3l - Exercise

      15. 3m - Solution

      16. 3n - Progress Checkpoint

      17. 3o - Model Pipelines

      18. 3p - Exercise

      19. 3q - Solution

      20. 3r - Progress Checkpoint

      21. 3s - Hyperparameter Tuning

      22. 3t - Exercise

      23. 3u - Solution

      24. 3v - Exercise

      25. 3w - Solution

      26. 3x - Aggregating Hyperparameter Grids

      27. 3y - Progress Checkpoint

      28. 3z - Cross Validation

      29. 3aa - Creating Untrained Models

      30. 3ab - Exercise

      31. 3ac - Solution

      32. 3ad - Training and Tuning Models

      33. 3ae - Exercise

      34. 3af - Solution

      35. 3ag - Model Evaluation

      36. 3ah - Exercise

      37. 3ai - Solution

      38. 3aj - Progress Checkpoint

      39. 3ak - Visualizing Model Predictions

      40. 3al - Exercise

      41. 3am - Solution

      42. 3an - Using Your Model

      43. 3ao - Review

      1. Learning Objectives

      2. 4a - Binary Classification

      3. 4b - Logistic Regression

      4. 4c - Decision Tree Classifiers

      1. Learning Objectives

      2. Download Exercise Notebook ML05

      3. 5a - Case Study Overview

      4. 5b - Exercise

      5. 5c - Solution

      6. 5d - Metadata

      7. 5e - Exercise

      8. 5f - Solution

      9. 5g - One Error

      10. 5h - Exercise

      11. 5i - Solution

      12. 5j - Countplot of Investors

      13. 5k - Exercise

      14. 5l - Solution

      15. 5m - Exploring Relationships

      16. 5n - Exercise

      17. 5o - Solution

      18. 5p - Reviewing Your Results

      19. 5q - Feature Engineering

      20. 5r - Exercise

      21. 5s - Solution

      22. 5t - Reviewing tier_change

      23. 5u - Controlling for Demotions

      24. 5v - Exercise

      25. 5w - Solution

      26. 5x - Analyzing Goldman Sachs

      27. 5y - Exercise

      28. 5z - Solution

      29. 5aa - Seaborn .Implot() Function

      30. 5ab - Exercise

      31. 5ac - Solution

      32. 5ad - Review

    About this course

    • $195.00
    • 156 lessons
    • 3 hours of video content

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