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Blockchain and Cryptocurrency Machine Learning - Build 12 Models, Decentralized Federated Learning and More
00a Course Overview
00 Course Overview - Blockchain Machine Learning (9:14)
Source Files
00b What is Blockchain
00 How Blockchain Was Invented (7:26)
01 Blockchain Introduction (8:32)
02 What Is Bitcoin Mining (5:11)
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00c Mammoth Interactive Courses Introduction
01 How To Learn Online Effectively (13:46)
00 About Mammoth Interactive (1:12)
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01 What is Machine Learning
01 What Is Machine Learning (5:26)
02 What Is Supervised Learning (10:39)
02 Introduction to Python (Prerequisite)
00. Intro To Course And Python (9:57)
01. Variables (19:19)
02. Type Conversion Examples (10:06)
03. Operators (28:54)
04. Collections (8:24)
05. List Examples (19:41)
06. Tuples Examples (8:36)
07. Dictionaries Examples (14:26)
08. Ranges Examples (8:32)
09. Conditionals (6:43)
10. If Statement Examples (21:32)
11. Loops (29:42)
12. Functions (17:01)
13. Parameters And Return Values Examples (13:54)
14. Classes And Objects (34:11)
15. Inheritance Examples (17:29)
16. Static Members Examples (11:05)
17. Summary And Outro (4:08)
Python_Language_Basics
Intro to Python Slides
03 Regression Machine Learning with Blockchain API
00A Project Preview (2:12)
00B What Is Linear Regression (5:03)
01 Collect Data From Blockchain Api (12:57)
02 Join CSV Files With Blockchain Data (9:01)
03 Process Data (4:06)
04 Visualize Data (11:19)
05 Create X And Y (6:15)
06 Build A Linear Regression Model (4:59)
07 Build A Polynomial Regression Model (5:53)
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04 Clustering Machine Learning on Cryptocurrencies
00A Project Preview (3:02)
00B What Is Unsupervised Learning (8:17)
01 Collect Crypto Data With Cryptocompare API (9:35)
02 Clean Data (8:10)
03 Process Text Features (7:26)
04A What Is Principal Component Analysis (7:27)
04B Reduce Data Dimensionality With Principal Component Analysis (4:41)
05A What Is K Means Clustering (11:58)
05B Cluster Cryptocurrencies With K-Means Clustering (7:41)
06 Machine Learning With Optimal Number Of Clusters (4:48)
07 Visualize Clusters (5:25)
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05a Build a K Nearest Neighbors Model
01 What Is K Nearest Neighbours (8:07)
02 Scrape Crypto Data With Yahoo Finance API (7:58)
03 Process Data (15:33)
04 Build A K-Nearest Neighbors Classifier (10:08)
05 Calculate Error For Different K Values (6:38)
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05b Build a Radius Neighbors Regression Model
00 What Is Radius Neighbors Machine Learning (5:03)
01 Load Stock Data With Yahoo Finance API (6:59)
02 Build X And Y Training And Testing Data (5:11)
03 Build A Radius Neighbors Regression Model (7:30)
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06a Build a CatBoost Model
00 What Is Catboost Machine Learning (2:26)
00B What Is Gradient Boosting (8:38)
01 Load Data (4:51)
02 Process Data (10:53)
03 Build A Catboost Classifier Model (7:31)
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06b Build an XGBoost Regression Model
00 What Is XGboost Machine Learning (1:31)
01 Load Stock Data With Yahoo Finance API (4:35)
02 Build An XGboost Regression Model (7:27)
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07a Neural Network Fundamentals
01 What Is Deep Learning (7:42)
02 What Is A Neural Network (8:47)
07b Build a Neural Network Classifier
01 Load Stock Data With Yahoo Finance API (7:20)
02 Build X And Y Training And Testing Data (6:13)
03 Build A Neural Network Classifier (6:29)
04 Calculate Neural Network Accuracy From Confusion Matrix (9:30)
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07c Build a Recurrent Neural Network with TensorFlow
00A Project Preview (2:12)
00B What Is A Recurrent Neural Network (6:38)
01 Load Stock Data With Yahoo Finance API (7:20)
02 Visualize Data (8:27)
03 Build A Training Dataset (8:04)
04 Build Features And Labels (10:37)
05 Build A Tensorflow Lstm Neural Network (12:04)
06 Load Test Data With An API (7:32)
07 Build Features And Labels For Testing The Neural Network (10:42)
08 Visualize Model's Predictions (8:42)
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08 Build a Bagging Classifier Model
00A Bagging And Decision Trees Introduction (5:25)
00B How Bagging Works (7:11)
01 Load Stock Data With Yahoo Finance API (8:34)
02 Build X And Y Training And Testing Data (6:00)
03 Train And Test A Bagging Classifier (7:34)
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09 Build a Light Gradient Boosted Regression Ensemble
00A Gradient Boosting Introduction (8:40)
00B What Is A Light Gradient Boosted Regression Ensemble (5:08)
01 Load Stock Data With Yahoo Finance API (5:08)
02 Build A Light GBM (7:59)
03 Find Best Number Of Trees (8:46)
04 Find Best Tree Depth (5:23)
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10 Build a Nested Cross Validation Model
00 What Is Nested Cross Validation (14:29)
01 Load Stock Data With Yahoo Finance Api (3:01)
02 Build More Features (6:32)
03 Define X And Y (5:55)
04 Implement Cross Validated Grid Search (6:02)
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11 Differential Privacy Project
00 What Is Differential Privacy (7:18)
01 Differential Privacy Project Introduction (13:16)
02 Build An Initial Database (3:05)
03 Build A Parallel Database (4:04)
04 Build Multiple Parallel Databases (3:09)
05 Determine If Query Leaks Private Data (5:12)
06 Calculate Sensitivity Of Mean Query (6:29)
07 Build Local Differential Privacy (9:09)
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12 Deep Learning Differential Privacy Project
00 Deep Learning Differential Privacy Introduction (13:22)
01 Build Database (3:45)
02 Build A Differential Privacy Query (4:10)
03 Perform Pate Analysis (6:10)
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13 Build a Federated Learning Model
00 What Is Federated Learning (6:28)
01 Generate A Dataset (10:03)
02 Build A Regular Model (7:43)
03 Visualize Model Results (7:01)
04 Build A Client-Side Model (2:51)
05 Build An Aggregator Model (2:07)
06 Generate Clients Dataset (9:26)
07 Execute The Federated Learning Model (9:58)
08 Evaluate The Model (3:36)
Source Files
01 Load Stock Data With Yahoo Finance API
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