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Learn to predict cryptocurrency future prices using the power of Python Machine Learning (Artificial Intelligence)

What you will learn

Learn to Predict Future Cryptocurrency Prices with ML Python Coding.

Learn to Build Machine Learning Models for Cryptocurrency Price Predictions.

Create a Web App that will Predict Cryptocurrency Future Prices.

Learn to Deploy Machine Learning Models for Cryptocurrency Price Prediction.

Description

It is a comprehensive course that shows how you can build a stylish web app with machine learning at the backend to predict the future price of any cryptocurrency. The main course has a mini crash course on Python for newbies and culminates into the theory and practice of Machine Learning and its predictive modeling application on cryptocurrencies.

At the end of this course, you will be able to develop a full-fledged web app that will take in data (available for free on the Internet). As you will provide the data to the web app, the web app having its predictive machine learning model at the backend will spit out the future prices of a cryptocurrency.

The course includes all the code for the web app, and with a tiny tuning in the code, you can adjust the web app to predict the prices of any cryptocurrency. And for any number of days in the future (recommended not to proceed more than 10-15 days for accuracy).


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All the tools, software, and data used in the course can be downloaded for free and put to use instantly.

This course builds Machine Learning models from three popular algorithms. With Python code taking advantage of the predictive nature of machine learning, that can detect patterns in data that humans are not capable of doing. All the models go through evaluation for their accuracy before deployment.

I hope you will be able to build bigger, better, more efficient, and more effective models and web apps to predict cryptocurrency future prices with more accuracy.

English
language

Content

Getting Started

Promo Video (Introduction)

Coding Environments

02 Setting up the Coding Environment – 1
02 Setting up the Coding Environment – 2

Python Crash Course

01 Python Crash Course – Overview
03 Python Comments
03 Python Variables
04 Python Functions and Methods
05 Data types in Python
06 Pandas DataFrame
07 CSV Data Files Format
08 Python Libraries

04 Machine Learning

01 Machine Learning – Introduction
02 Machine Learning for Crypto Trading
03 Machine Learning Algorithms 101
04 Machine Learning Models 101
05 ML Algorithms vs ML Models – The Difference
06 Regression vs Classification

05 Cryptocurrency Data Preprocessing & Analysis

01 Candlestick OHLC
02 Obtaining Crypto data Using yFinance
03 Data Inspection and visualization
04 Measuring Cryptocurrency Performance
05 Calculating Returns from Historical Data
05 Calculating Returns from Historical Data
06 Regularization or Normalization of Data
07 Covariance and Correlation

06 Performance Evaluation of Cryptocurrencies

01 Performance Evaluation (Risk-Return)
02 ETHUSD Data Download

07 Predicting Crypto Prices with Machine Learning

01 Simple Linear Regression
02 Predicting Crypto Prices – Simple Linear Regression
03 Support Vector Machine (SVM)
04 Predicting Crypto Prices – SVM
05 XGBoost
06 Predicting Crypto Prices – XGBoost

08 ML Model Evaluation

01 Residual Error
02 R Squred Error Intuition
03 R Squared Error Implementation

09 ML Model Deployment

09 ML Model Deployment