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 algorithm 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

    • 2

      Chapter 1: Data Cleaning and Exploration

      • Learning Objectives

      • Download Exercise Notebook ML01

      • 1a - The Machine Learning Process

      • 1b - Matplotlib and Seaborn

      • 1c - A Few Quick Notes

      • 1d - Exercise

      • 1e - Solution

      • 1f - Countplot Function

      • 1g - Exercise

      • 1h - Solution

      • 1i - Replace Function and Sparse Classes

      • 1j - Exercise

      • 1k - Solution

      • 1l - Exercise

      • 1m - Solution

      • 1n - Spotting Outliers

      • 1o - Exercise

      • 1p - Solution

      • 1q - Exercise

      • 1r - Solution

      • 1s - Exercise

      • 1t - Solution

      • 1u - Exercise

      • 1v - Solution

      • 1w - Exercise

      • 1x - Solution

      • 1y - NaN Object

      • 1z - Exercise

      • 1aa - Solution

      • 1ab - Dropping Null Values

      • 1ac - Exercise

      • 1ad - Solution

      • 1ae - Boxplots with Seaborn

      • 1af - Exercise

      • 1ag - Solution

      • 1ah - Saving Your DataFrame

      • 1ai - Review

    • 3

      Chapter 2: Regression Algorithms

      • Learning Objectives

      • 2a - What Are Regression Algorithms?

      • 2b - Real Relationships and Overfitting

      • 2c - Preventing Overfitting with Regularization

      • 2d - Decision Trees and Ensemble Methods

    • 4

      Chapter 3: Liquidity Regressor

      • Learning Objectives

      • Download Exercise Notebook ML03

      • 3a - Case Study Overview

      • 3b - Exercise

      • 3c - Solution

      • 3d - Metadata

      • 3e - Exercise

      • 3f - Solution

      • 3g - Splitting Your Data

      • 3h - Exercise

      • 3i - Solution

      • 3j - train_test_split() Function

      • 3k - Unpacking Lists

      • 3l - Exercise

      • 3m - Solution

      • 3n - Progress Checkpoint

      • 3o - Model Pipelines

      • 3p - Exercise

      • 3q - Solution

      • 3r - Progress Checkpoint

      • 3s - Hyperparameter Tuning

      • 3t - Exercise

      • 3u - Solution

      • 3v - Exercise

      • 3w - Solution

      • 3x - Aggregating Hyperparameter Grids

      • 3y - Progress Checkpoint

      • 3z - Cross Validation

      • 3aa - Creating Untrained Models

      • 3ab - Exercise

      • 3ac - Solution

      • 3ad - Training and Tuning Models

      • 3ae - Exercise

      • 3af - Solution

      • 3ag - Model Evaluation

      • 3ah - Exercise

      • 3ai - Solution

      • 3aj - Progress Checkpoint

      • 3ak - Visualizing Model Predictions

      • 3al - Exercise

      • 3am - Solution

      • 3an - Using Your Model

      • 3ao - Review

    • 5

      Chapter 4: Classification Algorithms

      • Learning Objectives

      • 4a - Binary Classification

      • 4b - Logistic Regression

      • 4c - Decision Tree Classifiers

    • 6

      Chapter 5: Investor Classifier I

      • Learning Objectives

      • Download Exercise Notebook ML05

      • 5a - Case Study Overview

      • 5b - Exercise

      • 5c - Solution

      • 5d - Metadata

      • 5e - Exercise

      • 5f - Solution

      • 5g - One Error

      • 5h - Exercise

      • 5i - Solution

      • 5j - Countplot of Investors

      • 5k - Exercise

      • 5l - Solution

      • 5m - Exploring Relationships

      • 5n - Exercise

      • 5o - Solution

      • 5p - Reviewing Your Results

      • 5q - Feature Engineering

      • 5r - Exercise

      • 5s - Solution

      • 5t - Reviewing tier_change

      • 5u - Controlling for Demotions

      • 5v - Exercise

      • 5w - Solution

      • 5x - Analyzing Goldman Sachs

      • 5y - Exercise

      • 5z - Solution

      • 5aa - Seaborn .Implot() Function

      • 5ab - Exercise

      • 5ac - Solution

      • 5ad - Review

    • 7

      Chapter 6: Investor Classifier II

      • Learning Objectives

      • Download Exercise Notebook ML06

      • 6a - Import Packages & Data

      • 6b - Exercise

      • 6c - Solution

      • 6d - Dummy Variables

      • 6e - Exercise

      • 6f - Solution

      • 6g - Remove Redundant Target

      • 6h - Splitting Data

      • 6i - Exercise

      • 6j - Solution

      • 6k - Model Pipelines

      • 6l - Exercise

      • 6m - Solution

      • 6n - Validating Pipelines

      • 6o - Hyperparameter Tuning

      • 6p - Exercise

      • 6q - Solution

      • 6r - Validating Hyperparameter Grids

      • 6s - Cross Validation

      • 6t - Exercise

      • 6u - Solution

      • 6v - Fitting Untrained Models

      • 6w - Exercise

      • 6x - Solution

      • 6y - AUROC Metric (Area Under ROC)

      • 6z - Confusion Matrix

      • 6aa - Perfect AUROC

      • 6ab - Calculating AUROC

      • 6ac - Exercise

      • 6ad - Solution

      • 6ae - Review

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