Machine Learning for Finance

Video description

Machine Learning techniques for solving major financial issues

About This Video

  • Sets a foundation of what to follow by teaching visualization and exploratory analysis of financial data, the typical features like RSI and moving average.
  • Predict stock prices by using Machine Learning models like Linear Regression, Random Forest, XGBoost and neural networks.
  • Use modern portfolio theory, Sharpe ratio, investment simulation, and machine learning to create a rewarding portfolio of stock investments.

In Detail

Machine Learning for Finance is a perfect course for financial professionals entering the fintech domain. It shows how to solve some of the most common and pressing issues facing institutions in the financial industry, from retail banks to hedge funds.

This video course focuses on Machine Learning and covers a range of analysis tools, such as NumPy, Matplotlib, and Pandas. It is packed full of hands-on code simulating many of the problems and providing working solutions.

This course aims to build your confidence and the experience to go ahead and tackle real-life problems in financial analysis. The industry is adopting automatic, data-driven algorithms at a rapid pace, and Machine Learning for Finance gives you the skills you need to be at the forefront.

By the end of this course, you will be equipped with all the tools from the world of Finance, machine learning and deep learning essential for tackling all these pressing issues in the area of Fintech.


This course is for financial professionals entering the field who already possess some Python skills and wish to become proficient in machine learning.

Requirements: Basic knowledge of Python, finance, and machine learning

Publisher resources

Download Example Code

Table of contents

  1. Chapter 1 : Financial Data Understanding, EDA, and Feature Engineering
    1. The Course Overview
    2. Visualization, EDA, and Feature Engineering of Financial Data
    3. Features of the Stock Data
    4. Univariate and Bivariate Analysis of Data
    5. Deriving Moving Average and RSI Based Features
    6. Data cleaning and Outlier Detection
    7. Creating the Features and Independent Variable
    8. Prepare Data for Modeling
  2. Chapter 2 : Predicting the FOREX Currencies by Building a Linear Model
    1. Linear Regression Intuition
    2. Understanding of FOREX Markets Data
    3. Pre-Process FOREX Currency Data for Model Input
    4. Building the Linear Regression Model
    5. R-Squared and Adjusted R-Squared as a Performance Metric
    6. The Testing Significance of Features by Using p-value and VIF
    7. Hyperparameter Tuning and Final Model Selection
  3. Chapter 3 : Tree-Based Machine Learning Techniques for Stock Prediction
    1. Decision Trees Intuition
    2. Entropy and Information Gain Criterion for Tree Construction
    3. Building a Decision Tree-Based Model for Predicting Stock Prices
    4. Train Using Different Max Depth
    5. Random Forest Intuition
    6. Build a Random Forest Regressor for Predicting Stock Prices
    7. Boosting and XGBoost Based Regression Model for Stock Prediction
  4. Chapter 4 : Artificial Neural Networks Basics and Intuition
    1. What a Neural Network Is
    2. Feed Forward in Neural Networks
    3. Gradient Descent in Neural Networks
    4. Back Propagation in Neural Networks
    5. Loss Function in Neural Networks
    6. Hyperparameters in Neural Networks
  5. Chapter 5 : Stock Price Prediction by Using Artificial Neural Networks
    1. Prepare Data for Ingestion into the Neural Network
    2. Define the Neural Network Layers and Model
    3. Visualize Keras Model by using Pydot
    4. Train the Model Using Basic Parameters
    5. Analyze the Model Performance Using Loss and Accuracy Curves
    6. Hyperparameter Tuning of Neural Network
    7. Generating Predictions by Using the Trained Model
  6. Chapter 6 : Modern Portfolio Theory and Techniques for Portfolio Management
    1. MPT and Stock Data Intuition
    2. Random Portfolio Generation and Portfolio Volatility
    3. Sharpe Ratio for Optimum Portfolio
    4. Portfolio Allocation Using Sharpe Ratio and Efficient Frontier
    5. Maximum Sharpe Ratio with SciPy Optimization
    6. Plotting and Visualizing Efficient Frontier
    7. Final Portfolio Allocation and Visualization
  7. Chapter 7 : Predicting Fraud in Financial Transactions by Using ANN classification
    1. Softmax and Sigmoid Activation in Neural Networks
    2. Categorical Cross Entropy Loss for Classification
    3. Feature Engineering and Preprocess Data for Input into the Model
    4. Creating the Model and the Optimizer
    5. Training the Model
    6. Handling Class Imbalance
    7. Evaluating the Final Model and Predict Fraud Using the Model

Product information

  • Title: Machine Learning for Finance
  • Author(s): Aryan Singh
  • Release date: April 2020
  • Publisher(s): Packt Publishing
  • ISBN: 9781789535143