AI for Finance

Name: AI for Finance
Rating: 4.3 (112 reviews)

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

Created byPackt Publishing

Last updated 3/2019

English

What you'll 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

Course content

5 sections • 18 lectures • 2h 19m total length

The Course Overview5:36
This video gives a glimpse of the entire course.
What’s Financial Forecasting and Why It’s Important?6:58
Define what we mean by financial forecasting, what AI methods we will be using in this course and how they solve common problems in Finance.
• Learn the basic definition of financial forecasting
• Learn which AI methods we will be focusing on in this course
• Learn how those methods help solving one of the most challenging problems in Finance
Installing Pandas, Scikit-Learn, Keras, and TensorFlow6:11
Learn how to quickly install and verify all the necessary tools to work with financial data and AI methods.
• Download, install, and verify Miniconda package manager and Python 3.7 distribution
Summary1:06
Sum up what we’ve learned in this section.
• The intuition behind financial forecasting
• Understanding why forecasting is a fundamental tool in Finance
• Learn how to quickly install all the necessary tools to work with AI methods and financial data

Getting and Preparing the Currency Exchange Data10:46
Learn where you can download the free stock prices data and how to convert for forecasting with a MLP Model.
• Locate and download the free stock prices data
• Explore the dataset
• Shape dataset into a supervised learning problem
Building the MLP Model with Keras10:03
Understand how to build a MLP Model for forecasting step by step.
• Learn the main container, input, and output of each MLP Model
• Learn how to add a hidden layer into an MLP Model
• Explore how to pick up the right loss function and optimizer and how to compile the model
Training and Testing the Model21:12
Learn the steps behind training and testing the MLP Model.
• Understand the key metrics in training and testing the model
• Learn when it’s a good time to stop training the model for optimal results
• Learn how use the training script and interpret the training results
Summary and What’s Next?3:47
Summarize what you’ve learned in this section.
• Understand the big picture being using a MLP Model for financial forecasting
• Learn where you can find the free stock prices data and how to prepare it for the MLP Model
• Explore the training process of the MLP Model step by step

Getting and Preparing the Loan Approval Data9:53
Learn where to get the rare loan financial dataset for free and how to shape it for our model.
• Locate and download the dataset
• Explore the dataset
• Encode the dataset for our classifier
Creating, Training, Testing, and Using a GradientBoostingClassfier Model7:31
Understand how to create a gradient boosted classifier in Scikit-Learn, train and evaluate the model.
• Create a new classifier
• Evaluate the classifier using our dataset and cross validation method
• Use our model on a new dataset
Summary and What’s Next?2:39
Summarize what you’ve learned in this section.
• Understand the high level of the problem
• Learn where you can get the problem dataset and how to use it for forecasting
• Build and evaluate the model, use it for forecasting with a new data

Getting and Preparing Financial Fraud Data9:51
Find the rare financial data and learn how to use with detecting frauds.
• Locate and download the data
• Explore the dataset
• Clean up and encode dataset for optimal results
Creating, Training, and Testing XGBoost Model7:19
Learn how to create, train and test a new model that is able to deal with an imbalanced dataset.
• Create and configure a new classifier in Scikit-Learn for an imbalanced dataset
• Train the new model
• Evaluate the model using a test set
Summary and What’s Next?2:46
Summarize what you’ve learned in this section.
• Understand the main steps in detecting fraud in financial data
• Explore the fraud dataset
• Learn how build, train, and test the model for fraud detection

Getting and Preparing the Stock Prices Data12:43
Find out where you can get the free sock prices data and how to format it for LSTM.
• Locate and download the free stock prices data, put it in the right place
• Explore the dataset
• Create a supervised learning problem dataset
Building the LSTM Model with Keras6:53
Understand the main steps to create a LSTM model in Keras.
Understand the main model container and it’s input and output
Learn how to configure a LSTM hidden layer
Pick up the right parameters to compile the model
Training and Testing the Model10:42
Learn the basics of training and testing the LSTM Model.
Understand the main training parameters like batch-size and epoch, pick up the right values
Understand the training and testing metrics and how to use them to find out when to stop training
Learn how to run the training script and interpret results
Summary and What’s Next?3:22
Summarize what you’ve learned in this section.
Understand the main goal of this section
Learn where to get the data and how to use it
Learn how to train a LSTM network with our dataset

Requirements

Some basic knowledge related to Python is assumed. However, no knowledge about financial data analysis is assumed.

Description

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.

About The Author

Jakub Konczyk has enjoyed and done programming professionally since 1995. He is a Python and Django expert and has been involved in building complex systems since 2006. He loves to simplify and teach programming subjects and share it with others. He first discovered Machine Learning when he was trying to predict the real estate prices in one of the early stage start-ups he was involved in. He failed miserably. Then he discovered a much more practical way to learn Machine Learning that he would like to share with you in this course. It boils down to “Keep it simple!” mantra.

Who this course is for:

This course is for aspiring data scientists, ML practitioners, as well as Investment Analysts and Portfolio managers working in the finance and investment industry.

AI for Finance

What you'll learn

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Course content

Introduction to Financial Forecasting4 lectures • 20min

Predicting Currency Exchange Rates with Multi-Layer Perceptron4 lectures • 46min

Loan Approval Prediction with GradientBoostingClassifier3 lectures • 20min

Detecting Fraud in Financial Services Using Extreme GradientBoostingClassifier3 lectures • 20min

Forecasting Stock Prices Using Long-Short Term Memory Network4 lectures • 34min

Requirements

Description

Who this course is for: