Machine Learning for Finance

Course Curriculum

Chapter 1: Financial Data Understanding, EDA, and Feature Engineering

Lecture 1: The Course Overview

Lecture 2: Visualization, EDA, and Feature Engineering of Financial Data

Lecture 3: Features of the Stock Data

Lecture 4: Univariate and Bivariate Analysis of Data

Lecture 5: Deriving Moving Average and RSI Based Features

Lecture 6: Data cleaning and Outlier Detection

Lecture 7: Creating the Features and Independent Variable

Lecture 8: Prepare Data for Modeling

Chapter 2: Predicting the FOREX Currencies by Building a Linear Model

Lecture 1: Linear Regression Intuition

Lecture 2: Understanding of FOREX Markets Data

Lecture 3: Pre-Process FOREX Currency Data for Model Input

Lecture 4: Building the Linear Regression Model

Lecture 5: R-Squared and Adjusted R-Squared as a Performance Metric

Lecture 6: The Testing Significance of Features by Using p-value and VIF

Lecture 7: Hyperparameter Tuning and Final Model Selection

Chapter 3: Tree-Based Machine Learning Techniques for Stock Prediction

Lecture 1: Decision Trees Intuition

Lecture 2: Entropy and Information Gain Criterion for Tree Construction

Lecture 3: Building a Decision Tree-Based Model for Predicting Stock Prices

Lecture 4: Train Using Different Max Depth

Lecture 5: Random Forest Intuition

Lecture 6: Build a Random Forest Regressor for Predicting Stock Prices

Lecture 7: Boosting and XGBoost Based Regression Model for Stock Prediction

Chapter 4: Artificial Neural Networks Basics and Intuition

Lecture 1: What a Neural Network Is

Lecture 2: Feed Forward in Neural Networks

Lecture 3: Gradient Descent in Neural Networks

Lecture 4: Back Propagation in Neural Networks

Lecture 5: Loss Function in Neural Networks

Lecture 6: Hyperparameters in Neural Networks

Chapter 5: Stock Price Prediction by Using Artificial Neural Networks

Lecture 1: Prepare Data for Ingestion into the Neural Network

Lecture 2: Define the Neural Network Layers and Model

Lecture 3: Visualize Keras Model by using Pydot

Lecture 4: Train the Model Using Basic Parameters

Lecture 5: Analyze the Model Performance Using Loss and Accuracy Curves

Lecture 6: Hyperparameter Tuning of Neural Network

Lecture 7: Generating Predictions by Using the Trained Model

Chapter 6: Modern Portfolio Theory and Techniques for Portfolio Management

Lecture 1: MPT and Stock Data Intuition

Lecture 2: Random Portfolio Generation and Portfolio Volatility

Lecture 3: Sharpe Ratio for Optimum Portfolio

Lecture 4: Portfolio Allocation Using Sharpe Ratio and Efficient Frontier

Lecture 5: Maximum Sharpe Ratio with SciPy Optimization

Lecture 6: Plotting and Visualizing Efficient Frontier

Lecture 7: Final Portfolio Allocation and Visualization

Chapter 7: Predicting Fraud in Financial Transactions by Using ANN classification

Lecture 1: Softmax and Sigmoid Activation in Neural Networks

Lecture 2: Categorical Cross Entropy Loss for Classification

Lecture 3: Feature Engineering and Preprocess Data for Input into the Model

Lecture 4: Creating the Model and the Optimizer

Lecture 5: Training the Model

Lecture 6: Handling Class Imbalance

Lecture 7: Evaluating the Final Model and Predict Fraud Using the Model