Financial Prediction with Python Data Science for Stocks (15 Hours)
Created by Team Mammoth
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108 lectures
01
Skill level
All levels
02
Sections
14
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Lectures
108
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Instructor
Team Mammoth
What's inside
This course includes.
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14
Sections
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Certificate of completion
Included
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Mobile and desktop access
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AI learning assistance
Included
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Course content
Curriculum & lectures.
+ Python Introduction 21 lectures Preview
00. Intro
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01. Introduction To Python
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02. Variables
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02b. Variables Examples
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03. Type Conversion Examples
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04. Operators
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05. Operators Examples
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06. Collections
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07. Lists
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08. Multidimensional List Examples
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09. Tuples Examples
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10. Dictionaries Examples
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11. Ranges Examples
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12. Conditionals
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13. If Statement Examples
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14. If Statement Variants Examples
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15. Loops
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16. While Loops Examples
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17. For Loops Examples
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18. Functions
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19. Functions Examples
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+ Machine Learning theory 11 lectures
00. Course Intro
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01. Quick Intro To Machine Learning
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02. Deep Dive Into Machine Learning
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03. Problems Solved With Machine Learning Part 1
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04. Problems Solved With Machine Learning Part 2
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05. Types Of Machine Learning
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06. How Machine Learning Works
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07. Common Machine Learning Structures
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08. Steps To Build A Machine Learning Program
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09. Summary And Outro
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Intro to Machine Learning Slides
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+ Stock Market Data Analysis and Visualization with Python, Pandas, NumPy, Seaborn and Matplotlib - Course Overview 1 lecture
00 Project Preview
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+ Compare Stocks and Returns 6 lectures
01 Fetch Stock Data
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02 Visualize Stock Data Features
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03 Calculate Daily Return
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04 Compare Returns Of Different Stocks
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05 Compare Closing Prices
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+ Calculate and Visualize Risk 5 lectures
01 Visualize Standard Deviation And Expected Returns
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02 Calculate Value At Risk
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03 Monte Carlo Analysis To Estimate Risk
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04 Visualize Price Distribution
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+ Build a Stock Ticker Dashboard Web App with Python, Dash and Pandas 6 lectures
00 Project Preview
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01 Import Stock Data
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02 Build A Dash Web App
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03 Build Stock And Date Range Pickers
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04 Show Stock Data In The Web App
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+ Algorithmic Trading with Python, Statistics and Pandas - Build Investing Strategies - Course Overview 1 lecture
00 Project Preview
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+ 01 Build Your First Investing Strategy 6 lectures
01 Make An API Call
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02 Convert Data To A Pandas Dataframe
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03 Batch Api Calls To Improve Performance
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04 Calculate The Number Of Shares To Buy
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05 Build An Excel File From The Pandas Dataframe
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03-Source Files
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+ 02 Find 50 Best Momentum Stocks with 2 Investing Strategies 11 lectures
00 Project 2 Preview
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01 Make An API Call
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02 Execute A Batch API Call
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03 Remove Low Momentum Stocks
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04 Calculate The Number Of Shares To Buy
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05 Find High Quality Momentum Stocks
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06 Calculate Momentum Percentiles
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08 Calculate New Number Of Shares To Buy
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07 Find The 50 Best Momentum Stocks
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09 Build An Excel File
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04-Source Files
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+ 03 Find 50 Best Value Stocks with 2 Investing Strategies 10 lectures
00 Project 3 Preview
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01 Build A Dataframe
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02 Remove Glamour Stocks
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03 Calculate The Number Of Shares To Buy
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04 Build A Composite Of Valuation Metrics
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05 Clean Dataframe
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06 Calculate Value Percentiles
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07 Find The 50 Best Value Stocks
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08 Calculate New Number Of Shares To Buy
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05-Source Files
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+ Predict Stock Trends with Twitter Sentiment Analysis Machine Learning 3 lectures
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01 Fetch Twitter Sentiment And Stock Prices Datasets
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02 Merge Datasets Into Dataframe
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+ 02 Build a Random Forest Classifier Machine Learning Model 10 lectures
01 Project Preview
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01 What Is The Random Forest Classifier Model
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02 Process Stock And Sentiment Dataframe
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03 Sort Polarity Into Positive, Negative And Neutral Sentiment-4
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04 Calculate Stock Trend (Rising Or Falling)-5
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05 Build A Binary Encoding Of Sentiment-6
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06 Split And Scale Data-7
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07 Build A Random Forest Classifier Model-8
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08 Evaluate The Model-9
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+ 03 Build a Gradient Boosting Classifier 6 lectures
00 Project Preview
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01 What Is Gradient Boosting
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02 Test Different Learning Rates
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03 Make A Prediction With A Gradient Boosting Classifier
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04 Evaluate The Model
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+ Feature Analysis and Data Science with Stocks for Beginners 11 lectures
Course Overview
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01 Load And Create Data
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02 Perform Exploratory Data Analysis
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03 Visualize Data With Different Plots
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04 Analyze Features With More Plots
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05 Build Plots With Seaborn
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06 Build A Bokeh Plot
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07 Build A 3D Scatter Plot
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08 Rank Feature Importance
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09 Compare Positive And Negative Returns
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Source Files
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Instructors
Taught by people who ship.
Team Mammoth
Instructor
Produced by a team of Mammoth Club industry experts. Over 14 years, Mammoth Club has built a global student community in 190+ countries with 9+ million courses sold, releasing over 1,000+ courses and 5,000+ hours of video content.
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