Machine Learning and Data Science for Stock Market Prediction
01
Skill level
All levels
02
Sections
16
03
Lectures
87
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Instructor
Team Mammoth
What's inside
This course includes.
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16
Sections
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87
Lectures
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107
Resources
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Certificate of completion
Included
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Mobile and desktop access
Included
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AI learning assistance
Included
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Course content
Curriculum & lectures.
+ 01 Mammoth Interactive Courses Introduction 3 lectures
00 About Mammoth Interactive
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01 How To Learn Online Effectively
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+ 02 Machine Learning Fundamentals 3 lectures
01 What Is Machine Learning
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02 Types Of Machine Learning Models
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03 What Is Supervised Learning
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+ 03 Introduction to Python (Prerequisite) 1 lecture
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+ 04a Introduction to Regression 2 lectures
00 Regression Applications in Finance
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+ 04b Predict Stocks with a Linear Regression Model 6 lectures
00 Project Preview
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01 What is Linear Regression
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02 Preprocess Data for Machine Learning
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03 Make a Prediction with Linear Regression
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04 Visualize Model Results
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+ 05 Predict Stocks with a Polynomial Regression Model 6 lectures
01 Project Preview
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02 Preprocess Data for Polynomial Regression
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03 Make a Prediction with a 1D Polynomial
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04 Make a Prediction with Higher Dimensionailty Polynomial Regression
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05 Find Best Polynomial Model
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+ 06 Build a Logistic Regression Model 7 lectures
00 Project Preview
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00 What is Logistic Regression
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02 Preprocess Data for Logistic Regression
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03 Make a Prediction with Logistic Regression
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04 Evaluate Model Results
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05 Analyze Model Metrics
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+ 06b Build an Isotonic Regression Model 6 lectures
00 Project Preview
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00 What is Isotonic Regression
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01 Load Data for Isotonic Regression
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02 Build an Isotonic Regression Model
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03 Train and Evaluate the Model
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+ 07a Introduction to Trees 2 lectures
00 Tree Applications in Finance
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+ 07b Build a Decision Tree Model 5 lectures
00 Project Preview
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01 Make Decisions with Decision Trees
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02 Preprocess Data for Decision Tree Classification
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03 Build a Decision Tree
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+ 08 Build a Random Forest Model 7 lectures
00 Project Preview
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01 What is the Random Forest Classifier Model
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02 Preprocess Data for Random Forest Classification
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03 Train a Random Forest Classifier
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04 Visualize Feature Importance
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05 Train Model on Most Important Features
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+ 09 Build a K Nearest Neighbors Model 7 lectures
00 Project Preview
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01a What is K Nearest Neighbours
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01b How k-NN Works
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02 Preprocess Data for K Nearest Neighbors
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03 Train a K Nearest Neighbors Classifier
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04 Visualize Accuracy of Different Models
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+ 10 Build a Clustering Classification Model 8 lectures
00 Project Preview
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01A What is Unsupervised Learning
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01B What is K Means Clustering
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02 Load Data
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03 Preprocess Data for Clustering
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04 Build K Means Clustering Models
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05 Visualize Clusters
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