Autoplay
Autocomplete
Previous Lesson
Complete and Continue
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
01 Load Data For Neural Network
Lesson content will be unlocked within 30 minutes.
Teachable is working on this bug.
No further action will be required on your part
.
Thank you for your patience