Machine Learning for Finance – Udeme

Machine Learning for Finance

Description

Machine Learning for Finance is a training course on machine learning techniques to solve major financial problems. This course is suitable for financial specialists who want to enter this field. In fact, this course shows you how you can solve common and effective problems that organizations face in the financial industry. This course focuses on machine learning and covers various analytical tools such as NumPy, Matplotlib and Pandas. This course is full of simulating different problems, which along with raising problems, practical solutions are also taught. At the end of the course, you will be equipped with all the tools of finance, machine learning and deep learning for use in economics and finance.

What you will learn in the Machine Learning for Finance course:

  • How to deal with financial issues and financial investment
  • Learn to review technical features, EDA and understand considerations for financial data
  • Building a model based on neural networks to predict product prices
  • Improve your machine learning skills with general effect models such as random forest and XGBoost
  • Improving your understanding of neural networks and building regression-based models
  • How to detect fake transactions by building a fraud detection model using classification models

Course details

Publisher: Udemy
Instructors: Pact Publishing
English language
Education level: Intermediate
Number of courses: 49
Duration: 4 hours and 30 minutes

Course topics:

Course prerequisites:

Basic knowledge of Python, finance, and machine learning.

Pictures

Machine Learning for Finance

Sample video

Installation guide

After Extract, view with your favorite Player.

Subtitle: None

Quality: 1080p

download link

Download part 1 – 1 GB

Download part 2 – 1 GB

Download part 3 – 1 GB

Download part 4 – 38 GB

Password file(s): www.downloadly.ir

Size

3 GB

4.1/5 – (2847 points)

Be the first to comment

Leave a Reply

Your email address will not be published.


*