Machine Learning Coding Interview Questions
01
Skill level
All levels
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Sections
23
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Lectures
104
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Instructor
Team Mammoth
What's inside
This course includes.
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23
Sections
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104
Lectures
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123
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
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Course content
Curriculum & lectures.
+ Welcome! 1 lecture
Prerequisites
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+ 01 (Prerequisite) Introduction to Machine Learning 9 lectures
00A What Is Machine Learning
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00B Types Of Machine Learning Models
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00C What Is Supervised Learning
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00D What Is Unsupervised Learning
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01 How Does A Machine Learning Agent Learn
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02 What Is Inductive Learning
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03 Performance Of A Machine Learning Algorithm
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04 Handle Noise In Data
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05 Powerful Tools With Machine Learning Libraries
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+ 02 (Prerequisite) Introduction to Python 1 lecture
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+ Regression Prerequisites 4 lectures
00 Regression Introduction
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01 What Is Regression
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02 What Is The Random Forest Classifier Model
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03 What Is Gradient Boosting
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+ Evaluation Prerequisites 2 lectures
01 Performance Of A Machine Learning Algorithm
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02 What Is Error
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+ Neural network pre-requisites 4 lectures
01 What Is Deep Learning
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02 What Is A Neural Network
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03 What Is Cross Validation
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04 What Is The Adam Optimizer
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+ 07 Calculate Moving Average on Stock Price 4 lectures
00 Advanced Stock Prediction Data Science Question Overview
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01 Load And Visualize Stock Data
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02 Calculate Moving Average On Stock Price
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+ 08 Evaluate Moving Average Trading Strategy Results 5 lectures
01 Visualize Moving Average Results
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02 What are crossovers in stock analysis
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03 Calculate stock trends with crossovers
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04 Compare smoothed data and original
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+ 09 Analyze stock data with Relative Strength Index 3 lectures
00 What is Relative Strength Index Technical Indicator
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01 Analyze stock data with Relative Strength Index
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03 Source Files
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+ 10 Analyze data with Moving Average Convergence Divergence 4 lectures
01 What is Moving Average Convergence Divergence
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02 Analyze data with Moving Average Convergence Divergence
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03 Visualize MACD results with Pyplot
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+ 11 Time Series Data - 01 Build Binary Time Series on Stock Data 3 lectures
00 Time Series Data Science Interview Question Overview
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01 What are Binary Time Series
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source files
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+ 11 - 02 Calculate Volume Shocks with Pandas 5 lectures
01 What Are Volume Shocks
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02 Calculate Volume Shocks With Pandas
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03 Calculate Volume Shock Direction With Python
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04 Visualize Volume Shocks With Pyplot
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+ 11 - 03 Calculate Stock Price Shocks 4 lectures
01 What are Price Shocks
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02 Calculate Price Shocks with Python
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03 Visualize Price Shocks with Pyplot
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+ 11 - 04 Calculate Pricing Black Swan Shocks 4 lectures
01 What are Pricing Black Swan Shocks
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02 Calculate Pricing Black Swan Shocks
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03 Visualize Pricing Black Swan Shocks
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+ 11 - 05 Calculate Pricing Shock Without Volume Shock 4 lectures
01 What are Pricing Shocks Without Volume Shock
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02 Calculate Pricing Shock Without Volume Shock
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03 Visualize Pricing Shock Without Volume Shock
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+ 12 Backtesting Data 4 lectures
00 Backtesting Data Science Interview Question Overview
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01 Simulate Moving Average Trading Strategy with Python Backtesting
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02 Perform Quantitative Analysis
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+ 13 Data Generation and Manipulation - 01 Generate property data with Pandas 6 lectures
00 Data Generation and Manipulation Question Overview
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01 Generate property data with Pandas
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02 Generate data within range
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03 Generate property type with probabilities
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04 Generate number of rooms based on property type
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+ 13 - 02 Generate and clean missing data 5 lectures
01 Simulate missing data with Python
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02 Clean dataset with Python
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03 Find incorrect data type values
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04 Change data type of column
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+ 13 - 03 Manipulate data with Pandas 4 lectures
01 Optimize column names in Pandas
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02 Convert int column to bool
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03 Perform one hot encoding
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+ 14 - Fix Corrupted Image Dataset Interview Question 4 lectures
00 Fix Corrupted Image Dataset Interview Question Overview
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01 Fix corrupted dataset
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02 Count number of values in array column
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source files
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