• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar
  • Skip to footer
Finance Train

Finance Train

High Quality tutorials for finance, risk, data science

  • Home
  • Data Science
  • CFA® Exam
  • PRM Exam
  • Tutorials
  • Careers
  • Products
  • Login

What is Machine Learning?

Data Science

This lesson is part 2 of 22 in the course Machine Learning in Finance Using Python

Machine Learning is the field which applies statistical analysis and computer science for employing algorithms that learn how to perform tasks such as prediction or classification of a target variable as well as grouping data. These algorithms learn from data and are widely diverse as they range from traditional statistical models based on inference to complex deep neural networks architectures.

Machine learning has many applications in quantitative finance and their techniques have evolved and improved very fast in recent years. Some examples of problems that could be addressed with machine learning are the following:

  • Prediction of future asset price movements
  • Prediction of liquidity movements due to redemption of capital in large funds 
  • Finding mispricing opportunities in niche markets
  • Categorize stocks for optimizing portfolio allocations
  • Image recognition for use in commodity supply/demand signals
  • Natural Language Processing to gauge market sentiment and create trading signals for asset price forecasting

The big three categories in the machine learning field are Supervised Learning, Unsupervised Learning and Reinforced Learning.  All three methods have their own procedures and are used for different tasks such as prediction, find data patterns, categorizing data among others.

In this tutorial, we will focus on Supervised and Unsupervised Learning and we will provide an explanation of both fields showing examples in python programming for a practical overview and giving theoretical explanation to understand how these models works.

Machine Learning Steps

Working in machine learning involves many skills not only from statistics and mathematics, but also in the field of data cleaning, and data preparation/preprocessing.  Along this tutorial, we will focus in data preprocessing, training and testing models, model selection, performance evaluation and hyper-parameter tuning (Change default values of parameters of the models). The end-to-end workflow in machine learning can be described by the following picture: 

These steps at first follow this order but it is very common that once we have the predictions of the model, we start to work again on it and re-process the data in order to make new features, remove non-significant features, modify model parameters (hyperparameter tuning) and change the algorithm of the model (model selection).

In the next section we will explain important steps on the first stages of a machine learning project.  Along the course we provide code examples using Scikit Learn library from Python that has already implemented a great number of machine learning algorithms. 

The Scikit Learn is the most used library for Machine Learning in Python which has an extensive API for running machine learning algorithms.

Previous Lesson

‹ Machine Learning with Python

Next Lesson

Data Preprocessing in Data Science and Machine Learning ›

Join Our Facebook Group - Finance, Risk and Data Science

Posts You May Like

How to Improve your Financial Health

CFA® Exam Overview and Guidelines (Updated for 2021)

Changing Themes (Look and Feel) in ggplot2 in R

Coordinates in ggplot2 in R

Facets for ggplot2 Charts in R (Faceting Layer)

Reader Interactions

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Primary Sidebar

In this Course

  • Machine Learning with Python
  • What is Machine Learning?
  • Data Preprocessing in Data Science and Machine Learning
  • Feature Selection in Machine Learning
  • Train-Test Datasets in Machine Learning
  • Evaluate Model Performance – Loss Function
  • Model Selection in Machine Learning
  • Bias Variance Trade Off
  • Supervised Learning Models
  • Multiple Linear Regression
  • Logistic Regression
  • Logistic Regression in Python using scikit-learn Package
  • Decision Trees in Machine Learning
  • Random Forest Algorithm in Python
  • Support Vector Machine Algorithm Explained
  • Multivariate Linear Regression in Python with scikit-learn Library
  • Classifier Model in Machine Learning Using Python
  • Cross Validation to Avoid Overfitting in Machine Learning
  • K-Fold Cross Validation Example Using Python scikit-learn
  • Unsupervised Learning Models
  • K-Means Algorithm Python Example
  • Neural Networks Overview

Latest Tutorials

    • Data Visualization with R
    • Derivatives with R
    • Machine Learning in Finance Using Python
    • Credit Risk Modelling in R
    • Quantitative Trading Strategies in R
    • Financial Time Series Analysis in R
    • VaR Mapping
    • Option Valuation
    • Financial Reporting Standards
    • Fraud
Facebook Group

Membership

Unlock full access to Finance Train and see the entire library of member-only content and resources.

Subscribe

Footer

Recent Posts

  • How to Improve your Financial Health
  • CFA® Exam Overview and Guidelines (Updated for 2021)
  • Changing Themes (Look and Feel) in ggplot2 in R
  • Coordinates in ggplot2 in R
  • Facets for ggplot2 Charts in R (Faceting Layer)

Products

  • Level I Authority for CFA® Exam
  • CFA Level I Practice Questions
  • CFA Level I Mock Exam
  • Level II Question Bank for CFA® Exam
  • PRM Exam 1 Practice Question Bank
  • All Products

Quick Links

  • Privacy Policy
  • Contact Us

CFA Institute does not endorse, promote or warrant the accuracy or quality of Finance Train. CFA® and Chartered Financial Analyst® are registered trademarks owned by CFA Institute.

Copyright © 2021 Finance Train. All rights reserved.

  • About Us
  • Privacy Policy
  • Contact Us