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Deep Learning and Machine Learning for Stocks Masterclass
01 Mammoth Interactive Courses Introduction
00 About Mammoth Interactive (1:12)
01 How To Learn Online Effectively (13:46)
Source Files
02 Machine Learning Fundamentals
01 What Is Machine Learning (5:26)
02 Types Of Machine Learning Models (12:17)
03 What Is Supervised Learning (10:39)
03 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)
Intro to Python Slides
Python_Language_Basics
04a Introduction to Regression
00 Regression Applications In Finance (6:44)
Source Files
04b Predict Stocks with a Linear Regression Model
00 Project Preview (1:46)
01 What Is Linear Regression (5:03)
02 Preprocess Data For Machine Learning (11:49)
03 Make A Prediction With Linear Regression (3:59)
04 Visualize Model Results (8:03)
Source Files
05 Predict Stocks with a Polynomial Regression Model
01 Project Preview (1:21)
02 Preprocess Data For Polynomial Regression (12:08)
03 Make A Prediction With A 1D Polynomial (3:38)
04 Make A Prediction With Higher Dimensionailty Polynomial Regression (7:31)
05 Find Best Polynomial Model (8:25)
Source Files
06 Build a Logistic Regression Model
00 Project Preview (1:48)
00 What Is Logistic Regression (4:32)
02 Preprocess Data For Logistic Regression (11:11)
03 Make A Prediction With Logistic Regression (4:28)
04 Evaluate Model Results (15:48)
05 Analyze Model Metrics (6:33)
Source Files
06b Build an Isotonic Regression Model
00 Project Preview (1:43)
00 What Is Isotonic Regression (2:27)
01 Load Data For Isotonic Regression (7:26)
02 Build An Isotonic Regression Model (6:33)
03 Train And Evaluate The Model (7:51)
Source Files
07a Introduction to Trees
00 Tree Applications In Finance (7:02)
Source Files
07b Build a Decision Tree Model
00 Project Preview (2:34)
01 Make Decisions With Decision Trees (10:51)
02 Preprocess Data For Decision Tree Classification (11:08)
03 Build A Decision Tree (11:29)
Source Files
08 Build a Random Forest Model
00 Project Preview (1:45)
01 What Is The Random Forest Classifier Model (5:42)
02 Preprocess Data For Random Forest Classification (9:20)
03 Train A Random Forest Classifier (13:08)
04 Visualize Feature Importance (3:58)
05 Train Model On Most Important Features (6:20)
Source Files
09 Build a K Nearest Neighbors Model
00 Project Preview (1:14)
01A What Is K Nearest Neighbours (8:07)
01B How K-Nn Works (4:02)
03 Train A K Nearest Neighbors Classifier (5:21)
04 Visualize Accuracy Of Different Models (9:43)
02 Preprocess Data For K Nearest Neighbors (8:10)
Source Files
10 Build a Clustering Classification Model
00 Project Preview (2:00)
01A What Is Unsupervised Learning (8:17)
01B What Is K Means Clustering (11:58)
02 Load Data (8:22)
03 Preprocess Data For Clustering (7:42)
04 Build K Means Clustering Models (6:27)
05 Visualize Clusters (10:30)
Source Files
13 Deep Learning Introduction
00 Neural Network Applications In Finance (7:05)
01 What Is Deep Learning (7:42)
02 What Is A Neural Network (8:47)
03 What Is A Bernoulli Restricted Boltzmann Machine (4:28)
14 Build a Bernoulli Restricted Boltzmann Machine (Neural Network)
00 Project Preview (1:56)
01 Load Data For Neural Network (9:28)
02 Build A Neural Network (16:40)
Source Files
15 Build a Neural Network Classifier
00 Project Preview (1:26)
01 Load Data For Classifier (7:01)
02 Build A Neural Network (7:17)
03 Evaluate The Neural Network (8:22)
Source Files
01B How K-Nn Works
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