Artificial Intelligence With Crypto Stocks

[LeeAndro]

Published 8/2022MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 4.01 GB | Duration: 11h 39m

Build 20 ML projects!​

What you'll learn
Build machine learning models
Code in Python
Scrape blockchain data
Build federated learning models
Requirements
No experience required
Description
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 dataBuild clustering models on Cryptocurrency data Build a K Nearest neighbors model on crypto data from Yahoo FinanceBuild a radius neighbors regression model on stock dataBuild a gradient boosting modelBuild a neural network to classify stock dataBuild a differential privacy project with a database Build a federated model And Much moreWe 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 finishThe 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 courseHow does life 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!

Overview

Section 1: 00a Course Overview

Lecture 1 00 Course Overview - Blockchain Machine Learning

Section 2: 00b What is Blockchain

Lecture 2 00 How Blockchain Was Invented

Lecture 3 01 Blockchain Introduction

Lecture 4 02 What Is Bitcoin Mining

Section 3: 00c Mammoth Interactive Courses Introduction

Lecture 5 00 About Mammoth Interactive

Lecture 6 01 How To Learn Online Effectively

Section 4: 01 What is Machine Learning

Lecture 7 01 What Is Machine Learning

Lecture 8 02 What Is Supervised Learning

Section 5: 03 Regression Machine Learning with Blockchain API

Lecture 9 00A Project Preview

Lecture 10 00B What Is Linear Regression

Lecture 11 01 Collect Data From Blockchain API

Lecture 12 02 Join CSV Files With Blockchain Data

Lecture 13 03 Process Data

Lecture 14 04 Visualize Data

Lecture 15 05 Create X And Y

Lecture 16 06 Build A Linear Regression Model

Lecture 17 07 Build A Polynomial Regression Model

Lecture 18 Source Files

Section 6: 04 Clustering Machine Learning on Cryptocurrencies

Lecture 19 00A Project Preview

Lecture 20 00B What Is Unsupervised Learning

Lecture 21 01 Collect Crypto Data With Cryptocompare API

Lecture 22 02 Clean Data

Lecture 23 03 Process Text Features

Lecture 24 04A What Is Principal Component Analysis

Lecture 25 04B Reduce Data Dimensionality With Principal Component Analysis

Lecture 26 05A What Is K Means Clustering

Lecture 27 05B Cluster Cryptocurrencies With K-Means Clustering

Lecture 28 06 Machine Learning With Optimal Number Of Clusters

Lecture 29 07 Visualize Clusters

Lecture 30 Source Files

Section 7: 05a Build a K Nearest Neighbors Model

Lecture 31 01 What Is K Nearest Neighbors

Lecture 32 02 Scrape Crypto Data With Yahoo Finance API

Lecture 33 03 Process Data

Lecture 34 04 Build A K-Nearest Neighbors Classifier

Lecture 35 05 Calculate Error For Different K Values

Lecture 36 Source Files

Section 8: 05b Build a Radius Neighbors Regression Model

Lecture 37 00 What Is Radius Neighbors Machine Learning

Lecture 38 01 Load Stock Data With Yahoo Finance API

Lecture 39 02 Build X And Y Training And Testing Data

Lecture 40 03 Build A Radius Neighbors Regression Model

Lecture 41 Source Files

Section 9: 06a Build a CatBoost Model

Lecture 42 00 What Is Catboost Machine Learning

Lecture 43 00B What Is Gradient Boosting

Lecture 44 01 Load Data

Lecture 45 02 Process Data

Lecture 46 03 Build A Catboost Classifier Model

Lecture 47 Source Files

Section 10: 06b Build an XGBoost Regression Model

Lecture 48 01 Load Stock Data With Yahoo Finance API

Lecture 49 02 Build An XGboost Regression Model

Lecture 50 Source Files

Section 11: 07a Neural Network Fundamentals

Lecture 51 01 What Is Deep Learning

Lecture 52 02 What Is A Neural Network

Section 12: 07b Build a Neural Network Classifier

Lecture 53 01 Load Stock Data With Yahoo Finance API

Lecture 54 02 Build X And Y Training And Testing Data

Lecture 55 03 Build A Neural Network Classifier

Lecture 56 04 Calculate Neural Network Accuracy From Confusion Matrix

Lecture 57 Source Files

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

Lecture 58 00A Project Preview

Lecture 59 00B What Is A Recurrent Neural Network

Lecture 60 01 Load Stock Data With Yahoo Finance API

Lecture 61 02 Visualize Data

Lecture 62 03 Build A Training Dataset

Lecture 63 04 Build Features And Labels

Lecture 64 05 Build A Tensorflow Lstm Neural Network

Lecture 65 06 Load Test Data With An API

Lecture 66 07 Build Features And Labels For Testing The Neural Network

Lecture 67 08 Visualize Model's Predictions

Lecture 68 Source Files

Section 14: 08 Build a Bagging Classifier Model

Lecture 69 00A Bagging And Decision Trees Introduction

Lecture 70 00B How Bagging Works

Lecture 71 01 Load Stock Data With Yahoo Finance API

Lecture 72 02 Build X And Y Training And Testing Data

Lecture 73 03 Train And Test A Bagging Classifier

Lecture 74 Source Files

Section 15: 09 Build a Light Gradient Boosted Regression Ensemble

Lecture 75 00A Gradient Boosting Introduction

Lecture 76 00B What Is A Light Gradient Boosted Regression Ensemble

Lecture 77 01 Load Stock Data With Yahoo Finance API

Lecture 78 02 Build A Light GBM

Lecture 79 03 Find Best Number Of Trees

Lecture 80 04 Find Best Tree Depth

Lecture 81 Source Files

Section 16: 10 Build a Nested Cross Validation Model

Lecture 82 00 What Is Nested Cross Validation

Lecture 83 01 Load Stock Data With Yahoo Finance Api

Lecture 84 02 Build More Features

Lecture 85 03 Define X And Y

Lecture 86 04 Implement Cross Validated Grid Search

Lecture 87 Source Files

Section 17: 11 Differential Privacy Project

Lecture 88 00 What Is Differential Privacy

Lecture 89 01 Differential Privacy Project Introduction

Lecture 90 02 Build An Initial Database

Lecture 91 03 Build A Parallel Database

Lecture 92 04 Build Multiple Parallel Databases

Lecture 93 05 Detee If Query Leaks Private Data

Lecture 94 06 Calculate Sensitivity Of Mean Query

Lecture 95 07 Build Local Differential Privacy

Lecture 96 Source Files

Section 18: 12 Deep Learning Differential Privacy Project

Lecture 97 00 Deep Learning Differential Privacy Introduction

Lecture 98 01 Build Database

Lecture 99 02 Build A Differential Privacy Query

Lecture 100 03 Perform Pate Analysis

Lecture 101 Source Files

Section 19: 13 Build a Federated Learning Model

Lecture 102 00 What Is Federated Learning

Lecture 103 01 Generate A Dataset

Lecture 104 02 Build A Regular Model

Lecture 105 03 Visualize Model Results

Lecture 106 04 Build A Client-Side Model

Lecture 107 05 Build An Aggregator Model

Lecture 108 06 Generate Clients Dataset

Lecture 109 07 Execute The Federated Learning Model

Lecture 110 08 Evaluate The Model

Lecture 111 Source Files

Anyone interested in machine learning on cryptocurrency

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