Artificial Intelligence with Crypto Stocks

Artificial Intelligence with Crypto Stocks, available at $54.99, has an average rating of 4.25, with 111 lectures, based on 4 reviews, and has 20 subscribers.

You will learn about Build machine learning models Code in Python Scrape blockchain data Build federated learning models This course is ideal for individuals who are Anyone interested in machine learning on cryptocurrency It is particularly useful for Anyone interested in machine learning on cryptocurrency.

Enroll now: Artificial Intelligence with Crypto Stocks

Summary

Title: Artificial Intelligence with Crypto Stocks

Price: $54.99

Average Rating: 4.25

Number of Lectures: 111

Number of Published Lectures: 111

Number of Curriculum Items: 111

Number of Published Curriculum Objects: 111

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Build machine learning models

Code in Python

Scrape blockchain data

Build federated learning models

Who Should Attend

Anyone interested in machine learning on cryptocurrency

Target Audiences

Anyone interested in machine learning on cryptocurrency

Artificial Intelligence with Crypto Stocks, get wild and crazy with all our cryptocurrency and blockchain lessons!

Some of our contents for this course include:

Build regression models with Blockchain data

Build clustering models on Cryptocurrency data

Build a K Nearest neighbors model on crypto data from Yahoo Finance

Build a radius neighbors regression model on stock data

Build a gradient boosting model

Build a neural network to classify stock data

Build a differential privacy project with a database

Build a federated model

And Much more

We will start you from the basics of what a blockchain is and then periodically expand your knowledge on it’s many different applications.

Alexandra Kropova is a software developer with extensive experience in full-stack web development, app development and game development. She has helped produce courses for Mammoth Interactive since 2016, including the Coding Interview series in Java, JavaScript, C++, C#, Python and Swift.

When does the course start and finish?

The course starts now and never ends! It is a completely self-paced online course – you decide when you start and when you finish.

How long do I have access to the course?

How does lifetime access sound?

After enrolling, you have unlimited access to this course for as long as you like – across any and all devices you own.

Don’t miss on this opportunity and get started today!

Course Curriculum

Chapter 1: 00a Course Overview

Lecture 1: 00 Course Overview – Blockchain Machine Learning

Chapter 2: 00b What is Blockchain

Lecture 1: 00 How Blockchain Was Invented

Lecture 2: 01 Blockchain Introduction

Lecture 3: 02 What Is Bitcoin Mining

Chapter 3: 00c Mammoth Interactive Courses Introduction

Lecture 1: 00 About Mammoth Interactive

Lecture 2: 01 How To Learn Online Effectively

Chapter 4: 01 What is Machine Learning

Lecture 1: 01 What Is Machine Learning

Lecture 2: 02 What Is Supervised Learning

Chapter 5: 03 Regression Machine Learning with Blockchain API

Lecture 1: 00A Project Preview

Lecture 2: 00B What Is Linear Regression

Lecture 3: 01 Collect Data From Blockchain API

Lecture 4: 02 Join CSV Files With Blockchain Data

Lecture 5: 03 Process Data

Lecture 6: 04 Visualize Data

Lecture 7: 05 Create X And Y

Lecture 8: 06 Build A Linear Regression Model

Lecture 9: 07 Build A Polynomial Regression Model

Lecture 10: Source Files

Chapter 6: 04 Clustering Machine Learning on Cryptocurrencies

Lecture 1: 00A Project Preview

Lecture 2: 00B What Is Unsupervised Learning

Lecture 3: 01 Collect Crypto Data With Cryptocompare API

Lecture 4: 02 Clean Data

Lecture 5: 03 Process Text Features

Lecture 6: 04A What Is Principal Component Analysis

Lecture 7: 04B Reduce Data Dimensionality With Principal Component Analysis

Lecture 8: 05A What Is K Means Clustering

Lecture 9: 05B Cluster Cryptocurrencies With K-Means Clustering

Lecture 10: 06 Machine Learning With Optimal Number Of Clusters

Lecture 11: 07 Visualize Clusters

Lecture 12: Source Files

Chapter 7: 05a Build a K Nearest Neighbors Model

Lecture 1: 01 What Is K Nearest Neighbors

Lecture 2: 02 Scrape Crypto Data With Yahoo Finance API

Lecture 3: 03 Process Data

Lecture 4: 04 Build A K-Nearest Neighbors Classifier

Lecture 5: 05 Calculate Error For Different K Values

Lecture 6: Source Files

Chapter 8: 05b Build a Radius Neighbors Regression Model

Lecture 1: 00 What Is Radius Neighbors Machine Learning

Lecture 2: 01 Load Stock Data With Yahoo Finance API

Lecture 3: 02 Build X And Y Training And Testing Data

Lecture 4: 03 Build A Radius Neighbors Regression Model

Lecture 5: Source Files

Chapter 9: 06a Build a CatBoost Model

Lecture 1: 00 What Is Catboost Machine Learning

Lecture 2: 00B What Is Gradient Boosting

Lecture 3: 01 Load Data

Lecture 4: 02 Process Data

Lecture 5: 03 Build A Catboost Classifier Model

Lecture 6: Source Files

Chapter 10: 06b Build an XGBoost Regression Model

Lecture 1: 01 Load Stock Data With Yahoo Finance API

Lecture 2: 02 Build An XGboost Regression Model

Lecture 3: Source Files

Chapter 11: 07a Neural Network Fundamentals

Lecture 1: 01 What Is Deep Learning

Lecture 2: 02 What Is A Neural Network

Chapter 12: 07b Build a Neural Network Classifier

Lecture 1: 01 Load Stock Data With Yahoo Finance API

Lecture 2: 02 Build X And Y Training And Testing Data

Lecture 3: 03 Build A Neural Network Classifier

Lecture 4: 04 Calculate Neural Network Accuracy From Confusion Matrix

Lecture 5: Source Files

Chapter 13: 07c Build a Recurrent Neural Network with TensorFlow

Lecture 1: 00A Project Preview

Lecture 2: 00B What Is A Recurrent Neural Network

Lecture 3: 01 Load Stock Data With Yahoo Finance API

Lecture 4: 02 Visualize Data

Lecture 5: 03 Build A Training Dataset

Lecture 6: 04 Build Features And Labels

Lecture 7: 05 Build A Tensorflow Lstm Neural Network

Lecture 8: 06 Load Test Data With An API

Lecture 9: 07 Build Features And Labels For Testing The Neural Network

Lecture 10: 08 Visualize Models Predictions

Lecture 11: Source Files

Chapter 14: 08 Build a Bagging Classifier Model

Lecture 1: 00A Bagging And Decision Trees Introduction

Lecture 2: 00B How Bagging Works

Lecture 3: 01 Load Stock Data With Yahoo Finance API

Lecture 4: 02 Build X And Y Training And Testing Data

Lecture 5: 03 Train And Test A Bagging Classifier

Lecture 6: Source Files

Chapter 15: 09 Build a Light Gradient Boosted Regression Ensemble

Lecture 1: 00A Gradient Boosting Introduction

Lecture 2: 00B What Is A Light Gradient Boosted Regression Ensemble

Lecture 3: 01 Load Stock Data With Yahoo Finance API

Lecture 4: 02 Build A Light GBM

Lecture 5: 03 Find Best Number Of Trees

Lecture 6: 04 Find Best Tree Depth

Lecture 7: Source Files

Chapter 16: 10 Build a Nested Cross Validation Model

Lecture 1: 00 What Is Nested Cross Validation

Lecture 2: 01 Load Stock Data With Yahoo Finance Api

Lecture 3: 02 Build More Features

Instructors

Mammoth Interactive
Top-Rated Instructor, 3.3 Million+ Students

John Bura
Best Selling Instructor Web/App/Game Developer 1Mil Students

Rating Distribution

1 stars: 0 votes

2 stars: 0 votes

3 stars: 1 votes

4 stars: 1 votes

5 stars: 2 votes

Frequently Asked Questions

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