Description
Machine Learning Applied to Stock & Crypto Trading – Python is the name of the machine learning course applied to stock and cryptocurrency trading with Python, published by Udemy Academy. This course teaches you to excel in financial trading by applying machine learning techniques to financial data using Python. This course does not cover a lot of deep theories.
This is a very hands-on course, with high-level theory for anyone who can easily grasp the basic concepts, but more importantly, understand the program and use it immediately. If you are looking for a course with a lot of math, this course is not for you. If you are looking for a course to experience using financial data in a fun, exciting, and potentially profitable way, you will probably enjoy this course very much.
What you will learn in the Machine Learning Applied to Stock & Crypto Trading – Python course:
- Understand hidden states and regimes for any market or asset using hidden Markov models
- Discover optimal assets to trade in ETFs, stocks, forex, or crypto using K-Means Clustering
- Compress information from a wide set of indicators with PCA
- Make objective future predictions on financial data with xgboost
- Train a reinforcement learning AI model for stock trading with PPO
- Determine the market efficiency of each asset
- Learn about Python libraries including pandas, PyTorch (for deep learning), and sklearn.
Course details
Publisher: Udemy
Instructor: Shaun McDonogh
Language: English
Education level: Introductory to advanced
Number of lessons: 108
Duration of education: 17 hours and 24 minutes
Machine Learning Applied to Stock Course headings
Machine Learning Applied to Stock Course prerequisites
. You should be aware of trading-related concepts like Pairs Trading
. You should have awareness of assets like ETFs, the VIX, Stocks, and Crypto.
Course images
Instructions for use and tips
After Extract, view it with your favorite Player.
Subtitle: None.
Quality: 720p
download link
Download part 1 – 2 GB
Download part 2 – 2 GB
Download part 3 – 2 GB
Download part 4 – 2 GB
Download part 5 – 54 MB
file password link
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