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Financial Prediction with Python Data Science for Stocks (15 Hours)
Mammoth Interactive Courses Introduction
Preface
00 About Mammoth Interactive (1:12)
01 How To Learn Online Effectively (13:46)
Source Files
(Prerequisite) Introduction to Python
Introduction (4:42)
Variables (19:17)
Type Conversion Examples (10:04)
Operators (7:04)
Operators Examples (21:52)
Collections (8:23)
Lists (11:38)
Multidimensional List Examples (8:05)
Tuples Examples (8:34)
Dictionaries Examples (14:24)
Ranges Examples (8:30)
Conditionals (6:41)
If Statement Examples (10:16)
If Statement Variants Examples (11:18)
Loops (7:00)
While Loops Examples (11:30)
For Loops Examples (11:18)
Functions (7:47)
Functions Examples (9:16)
Parameters And Return Values Examples (13:46)
Classes And Objects (11:13)
Classes Example (13:11)
Objects Examples (9:54)
Inheritance Examples (17:26)
Static Members Example (11:03)
Summary And Outro (4:06)
Source code
(Prerequisite) Introduction to Machine Learning
What Is Machine Learning (5:26)
Types Of Machine Learning Models (12:17)
What Is Supervised Learning (11:04)
What Is Unsupervised Learning (8:17)
How Does A Machine Learning Agent Learn (7:38)
What Is Inductive Learning (4:11)
Handle Noise In Data (5:22)
Powerful Tools With Machine Learning Libraries- (12:11)
Performance Of A Machine Learning Algorithm (4:14)
Stock Market Data Analysis and Visualization with Python, Pandas, NumPy, Seaborn and Matplotlib
Fetch Stock Data (9:12)
Project Preview (3:17)
Visualize Stock Data Features (7:32)
Calculate Daily Return (3:27)
Compare Returns Of Different Stocks (10:45)
Compare Closing Prices (8:48)
Visualize Standard Deviation And Expected Returns (5:44)
Calculate Value At Risk (3:52)
Monte Carlo Analysis To Estimate Risk (9:11)
Visualize Price Distribution (9:07)
Source Files
Build a Stock Ticker Dashboard Web App with Python, Dash and Pandas
Project Preview (3:00)
Import Stock Data (7:47)
Build A Dash Web App (6:19)
Build Stock And Date Range Pickers (10:04)
Show Stock Data In The Web App (14:51)
Source Files
Algorithmic Trading with Python, Statistics and Pandas - Build Investing Strategies
Make An API Call (6:18)
Project Preview (1:58)
Convert Data To A Pandas Dataframe (9:41)
Batch Api Calls To Improve Performance (11:23)
Calculate The Number Of Shares To Buy (7:18)
Build An Excel File From The Pandas Dataframe (4:18)
Project 2 Preview (2:42)
Make An API Call (9:47)
Execute A Batch API Call (14:59)
Remove Low Momentum Stocks (6:06)
Calculate The Number Of Shares To Buy (7:53)
Find High Quality Momentum Stocks (11:42)
Calculate Momentum Percentiles (7:19)
Find The 50 Best Momentum Stocks (8:10)
Calculate New Number Of Shares To Buy (5:30)
Build An Excel File (3:41)
Project 3 Preview (1:55)
Build A Dataframe (5:58)
Remove Glamour Stocks (5:01)
Calculate The Number Of Shares To Buy (3:51)
Build A Composite Of Valuation Metrics (15:22)
Clean Dataframe (5:49)
Calculate Value Percentiles (4:50)
Find The 50 Best Value Stocks (6:58)
Calculate New Number Of Shares To Buy (3:24)
Source Files
Predict Stock Trends with Twitter Sentiment Analysis ML
Fetch Twitter Sentiment And Stock Prices Datasets (5:34)
Merge Datasets Into Dataframe (11:46)
Project Preview (2:27)
What Is The Random Forest Classifier Model (5:42)
Process Stock And Sentiment Dataframe (5:48)
Processing And Sorting Sentiment Data-1 (14:40)
Calculate Stock Trend (Rising Or Falling) (8:16)
Build A Binary Encoding Of Sentiment (5:14)
Split And Scale Data (13:56)
Build A Random Forest Classifier Model (13:06)
Evaluate The Model (6:48)
Project Preview (1:33)
What Is Gradient Boosting (1:56)
Test Different Learning Rates (8:09)
Make A Prediction With A Gradient Boosting Classifier (7:13)
Evaluate The Model (3:20)
Source Files
Feature Analysis and Data Science with Stocks for Beginners
Load And Create Data (9:55)
Perform Exploratory Data Analysis (3:41)
Visualize Data With Different Plots (11:06)
Analyze Features With More Plots (6:17)
Build Plots With Seaborn (4:35)
Build A Bokeh Plot (6:16)
Build A 3D Scatter Plot (3:45)
Rank Feature Importance (7:13)
Compare Positive And Negative Returns (8:06)
Course Overview (3:51)
Source Files
Test Different Learning Rates
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