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.

Publisher resources

Download Example Code

Table of contents

  1. Chapter 1 : Financial Data Understanding, EDA, and Feature Engineering
    1. The Course Overview 00:06:04
    2. Visualization, EDA, and Feature Engineering of Financial Data 00:06:40
    3. Features of the Stock Data 00:04:32
    4. Univariate and Bivariate Analysis of Data 00:14:29
    5. Deriving Moving Average and RSI Based Features 00:06:03
    6. Data cleaning and Outlier Detection 00:06:20
    7. Creating the Features and Independent Variable 00:06:37
    8. Prepare Data for Modeling 00:03:38
  2. Chapter 2 : Predicting the FOREX Currencies by Building a Linear Model
    1. Linear Regression Intuition 00:05:41
    2. Understanding of FOREX Markets Data 00:04:14
    3. Pre-Process FOREX Currency Data for Model Input 00:10:48
    4. Building the Linear Regression Model 00:06:55
    5. R-Squared and Adjusted R-Squared as a Performance Metric 00:04:19
    6. The Testing Significance of Features by Using p-value and VIF 00:08:12
    7. Hyperparameter Tuning and Final Model Selection 00:08:26
  3. Chapter 3 : Tree-Based Machine Learning Techniques for Stock Prediction
    1. Decision Trees Intuition 00:04:55
    2. Entropy and Information Gain Criterion for Tree Construction 00:07:06
    3. Building a Decision Tree-Based Model for Predicting Stock Prices 00:03:06
    4. Train Using Different Max Depth 00:04:13
    5. Random Forest Intuition 00:03:22
    6. Build a Random Forest Regressor for Predicting Stock Prices 00:03:54
    7. Boosting and XGBoost Based Regression Model for Stock Prediction 00:04:34
  4. Chapter 4 : Artificial Neural Networks Basics and Intuition
    1. What a Neural Network Is 00:08:40
    2. Feed Forward in Neural Networks 00:04:32
    3. Gradient Descent in Neural Networks 00:05:42
    4. Back Propagation in Neural Networks 00:07:53
    5. Loss Function in Neural Networks 00:04:09
    6. Hyperparameters in Neural Networks 00:08:00
  5. Chapter 5 : Stock Price Prediction by Using Artificial Neural Networks
    1. Prepare Data for Ingestion into the Neural Network 00:03:27
    2. Define the Neural Network Layers and Model 00:02:41
    3. Visualize Keras Model by using Pydot 00:02:52
    4. Train the Model Using Basic Parameters 00:02:12
    5. Analyze the Model Performance Using Loss and Accuracy Curves 00:01:34
    6. Hyperparameter Tuning of Neural Network 00:08:02
    7. Generating Predictions by Using the Trained Model 00:02:47
  6. Chapter 6 : Modern Portfolio Theory and Techniques for Portfolio Management
    1. MPT and Stock Data Intuition 00:08:12
    2. Random Portfolio Generation and Portfolio Volatility 00:07:20
    3. Sharpe Ratio for Optimum Portfolio 00:03:45
    4. Portfolio Allocation Using Sharpe Ratio and Efficient Frontier 00:07:20
    5. Maximum Sharpe Ratio with SciPy Optimization 00:04:57
    6. Plotting and Visualizing Efficient Frontier 00:08:34
    7. Final Portfolio Allocation and Visualization 00:06:25
  7. Chapter 7 : Predicting Fraud in Financial Transactions by Using ANN classification
    1. Softmax and Sigmoid Activation in Neural Networks 00:02:44
    2. Categorical Cross Entropy Loss for Classification 00:04:01
    3. Feature Engineering and Preprocess Data for Input into the Model 00:04:56
    4. Creating the Model and the Optimizer 00:02:24
    5. Training the Model 00:05:07
    6. Handling Class Imbalance 00:02:59
    7. Evaluating the Final Model and Predict Fraud Using the Model 00:05:27

Product information

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