Crypto Data Science and ML with Python

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

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: 01 What is Machine Learning

Lecture 1: 01 What Is Machine Learning

Lecture 2: 02 What Is Supervised Learning

Chapter 4: 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

Chapter 5: 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

Chapter 6: 05a Build a K Nearest Neighbors Model

Lecture 1: 01 What Is K Nearest Neighbours

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

Chapter 7: 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

Chapter 8: 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

Chapter 9: 06b Build an XGBoost Regression Model

Lecture 1: 01 Load Stock Data With Yahoo Finance API

Lecture 2: 02 Build An XGboost Regression Model

Chapter 10: 07a Neural Network Fundamentals

Lecture 1: 01 What Is Deep Learning

Lecture 2: 02 What Is A Neural Network

Chapter 11: 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

Chapter 12: 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

Chapter 13: 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

Chapter 14: 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

Chapter 15: 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

Lecture 4: 03 Define X And Y

Lecture 5: 04 Implement Cross Validated Grid Search

Chapter 16: 11 Differential Privacy Project

Lecture 1: 00 What Is Differential Privacy

Lecture 2: 01 Differential Privacy Project Introduction

Lecture 3: 02 Build An Initial Database

Lecture 4: 03 Build A Parallel Database

Lecture 5: 04 Build Multiple Parallel Databases

Lecture 6: 05 Determine If Query Leaks Private Data

Lecture 7: 06 Calculate Sensitivity Of Mean Query

Lecture 8: 07 Build Local Differential Privacy

Chapter 17: 12 Deep Learning Differential Privacy Project

Lecture 1: 00 Deep Learning Differential Privacy Introduction