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:
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Build regression models with Blockchain data
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Build clustering models on Cryptocurrency data
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Build a K Nearest neighbors model on crypto data from Yahoo Finance
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Build a radius neighbors regression model on stock data
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Build a gradient boosting model
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Build a neural network to classify stock data
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Build a differential privacy project with a database
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Build a federated model
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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 Model's 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
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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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