AI for Finance

AI for Finance
AI for Finance
English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 19m | 654 MB

Explore Machine Learning methods to predict future financial events based on past data

A lot of solutions to key problems in the financial world require predicting the future patterns in data from the past to make better financial decisions right now. The evolution of modern machine learning methods and tools in recent years in the field of computer vision bring promise of the same progress in other important fields such as financial forecasting.

In this course, you’ll first learn how to quickly get started with ML in finances by predicting the future currency exchange rates using a simple modern machine learning method. In this example, you’ll learn how to choose the basic data preparation method and model and then how to improve them. In the next module, you’ll discover a variety of ways to prepare data and then see how they influence models training accuracy. In the last module, you’ll learn how to find and test a few key modern machine learning models to pick up the best performing one.

After finishing this course, you’ll have a solid introduction to apply ML methods to financial data forecasting.

This video course offers a project-based approach with practical explanations to make sure you grasp the key aspects of each project and give you the skills required to develop financial forecasting tools in Python.

What You Will Learn

  • Get hands-on financial forecasting experience using machine learning with Python, Keras, Scikit-Learn and pandas
  • Use a variety of data preparation methods with financial data
  • Predict future values based on single and multiple values
  • Apply key modern Machine Learning methods for forecasting
  • Understand the process behind choosing the best performing data preparation method and model
  • Grasp Machine Learning forecasting on a specific real-world financial data
Table of Contents

Introduction to Financial Forecasting
1 The Course Overview
2 What’s Financial Forecasting and Why It’s Important
3 Installing Pandas, Scikit-Learn, Keras, and TensorFlow
4 Summary

Predicting Currency Exchange Rates with Multi-Layer Perceptron
5 Getting and Preparing the Currency Exchange Data
6 Building the MLP Model with Keras
7 Training and Testing the Model
8 Summary and What’s Next

Loan Approval Prediction with GradientBoostingClassifier
9 Getting and Preparing the Loan Approval Data
10 Creating, Training, Testing, and Using a GradientBoostingClassfier Model
11 Summary and What’s Next

Detecting Fraud in Financial Services Using Extreme GradientBoostingClassifier
12 Getting and Preparing Financial Fraud Data
13 Creating, Training, and Testing XGBoost Model
14 Summary and What’s Next

Forecasting Stock Prices Using Long-Short Term Memory Network
15 Getting and Preparing the Stock Prices Data
16 Building the LSTM Model with Keras
17 Training and Testing the Model
18 Summary and What’s Next