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Machine Learning Coding Interview Questions
00 Question Overview
Source files
00 Question Overview - Beginner Price Prediction (4:41)
01 (Prerequisite) Introduction to Machine Learning
00B Types Of Machine Learning Models (12:17)
00A What Is Machine Learning (5:26)
00C What Is Supervised Learning (11:04)
00D What Is Unsupervised Learning (8:17)
01 How Does A Machine Learning Agent Learn (7:38)
02 What Is Inductive Learning (4:11)
03 Performance Of A Machine Learning Algorithm (4:14)
04 Handle Noise In Data (5:22)
05 Powerful Tools With Machine Learning Libraries- (12:11)
02 (Prerequisite) Introduction to Python
00. Introduction (4:42)
01. Intro To Python (5:46)
01.01 What Is Google Colab (4:24)
01.02 What If I Get Errors (2:40)
01.03 How Do I Terminate A Session (2:40)
02. Variables (19:17)
02.04 How Do I Enable Corgi Mode (4:47)
02b. Variables Examples-33 (10:42)
03. Type Conversion Examples (10:04)
04. Operators (7:04)
05. Operators Examples (21:52)
06. Collections (8:23)
07. Lists (11:38)
08. Multidimensional List Examples (8:05)
09. Tuples Examples (8:34)
10. Dictionaries Examples (14:24)
11. Ranges Examples (8:30)
12. Conditionals (6:41)
13. If Statement Examples (10:16)
14. If Statement Variants Examples (11:18)
15. Loops (7:00)
16. While Loops Examples (11:30)
17. For Loops Examples (11:18)
18. Functions (7:47)
19. Functions Examples (9:16)
20. Parameters And Return Values Examples (13:46)
21. Classes And Objects (11:13)
22. Classes Example (13:11)
23. Objects Examples (9:54)
24. Inheritance Examples (17:26)
25. Static Members Example (11:03)
26. Summary And Outro (4:06)
Source code
(Pre-requisitie) Pandas
00. Panda Course Introduction (5:43)
01. Intro To Pandas (7:55)
02. Installing Pandas (5:28)
03. Creating Pandas Series (20:34)
04. Date Ranges (11:29)
05. Getting Elements From Series (19:21)
06. Getting Properties Of Series (13:04)
07. Modifying Series (19:02)
08. Operations On Series (11:48)
09. Creating Pandas Dataframes (22:57)
10. Getting Elements From Dataframes (25:12)
11. Getting Properties From Dataframes (17:44)
12. Dataframe Modification (36:24)
13. Dataframe Operations (20:09)
14 Dataframe Comparisons And Iteration (15:35)
15. Reading Csvs (12:00)
16.Summary And Outro (4:14)
Source Files
02 Perform exploratory data analysis
01 Generate Data With Chatgpt And Python (5:56)
02 Perform Exploratory Data Analysis (11:57)
Source
03 Transform categorical variables
01 Transform Categorical Variables (3:11)
00 (Prerequisite) What Is Data Encoding (5:36)
Source
04 Scale numerical features
00 (Prerequisite) What Is Scaling Data (6:36)
01 Scale Numerical Features (5:37)
Source Files
Regression Prerequisites
01 What Is Regression (19:55)
00 Regression Introduction (8:58)
02 What Is The Random Forest Classifier Model (5:42)
03 What Is Gradient Boosting (1:56)
Evalutaion Prerequisites
02 What Is Error (6:33)
01 Performance Of A Machine Learning Algorithm (4:09)
05 Build regression models
Source Files
01 Build Regression Models (9:12)
Neural network pre-requisites
01 What Is Deep Learning (7:37)
02 What Is A Neural Network (8:41)
03 What Is Cross Validation (8:20)
04 What Is The Adam Optimizer (6:10)
06 Build a neural network
02 Comparing Model Techniques (3:17)
01 Build A Neural Network (7:48)
Source
07 Calculate Moving Average on Stock Price
01 Load And Visualize Stock Data (11:40)
00 Advanced Stock Prediction Data Science Question Overview_1 (5:07)
02 Calculate Moving Average On Stock Price (6:52)
Resources
08 Evaluate Moving Average Trading Strategy Results
02 What Are Crossovers In Stock Analysis (4:31)
01 Visualize Moving Average Results (7:46)
03 Calculate Stock Trends With Crossovers (9:40)
04 Compare Smoothed Data And Original (5:57)
Source Files
09 Analyze stock data with Relative Strength Index
01 Analyze Stock Data With Relative Strength Index (18:06)
00 What Is Relative Strength Index Technical Indicator (2:38)
03 Source Files
10 Analyze data with Moving Average Convergence Divergence
01 What Is Moving Average Convergence Divergence (3:20)
02 Analyze Data With Moving Average Convergence Divergence (6:03)
03 Visualize Macd Results With Pyplot (9:43)
Source Files
11 Time Series Data - 01 Build Binary Time Series on Stock Data
00 Time Series Data Science Interview Question Overview_1 (4:19)
01 What Are Binary Time Series (2:21)
source files
11 - 02 Calculate Volume Shocks with Pandas
01 What Are Volume Shocks (2:18)
02 Calculate Volume Shocks With Pandas (4:34)
03 Calculate Volume Shock Direction With Python (4:31)
04 Visualize Volume Shocks With Pyplot (8:20)
source files
11 - 03 Calculate Stock Price Shocks
02 Calculate Price Shocks With Python (3:20)
01 What Are Price Shocks (2:18)
03 Visualize Price Shocks With Pyplot (5:07)
source files
11 - 04 Calculate Pricing Black Swan Shocks
02 Calculate Pricing Black Swan Shocks (4:04)
01 What Are Pricing Black Swan Shocks (2:25)
03 Visualize Pricing Black Swan Shocks (8:39)
source files
11 - 05 Calculate Pricing Shock Without Volume Shock
02 Calculate Pricing Shock Without Volume Shock (3:20)
01 What Are Pricing Shocks Without Volume Shock (1:58)
03 Visualize Pricing Shock Without Volume Shock (2:35)
source files
12 Backtesting Data
00 Backtesting Data Science Interview Question Overview (3:11)
01 Simulate Moving Average Trading Strategy With Python Backtesting (10:57)
02 Perform Quantitative Analysis (4:52)
Source Files
13 Data Generation and Manipulation - 01 Generate property data with Pandas
00 Data Generation And Manipulation Question Overview_1 (8:14)
01 Generate Property Data With Pandas (9:20)
02 Generate Data Within Range (6:43)
03 Generate Property Type With Probabilities (2:24)
04 Generate Number Of Rooms Based On Property Type (7:20)
Source Games
13 - 02 Generate and clean missing data
01 Simulate Missing Data With Python (3:59)
02 Clean Dataset With Python (6:46)
03 Find Incorrect Data Type Values (6:07)
04 Change Data Type Of Column (3:50)
Source Files
13 - 03 Manipulate data with Pandas
02 Convert Int Column To Bool (3:41)
01 Optimize Column Names In Pandas (5:05)
03 Perform One Hot Encoding (3:17)
Source files
14 - Fix Corrupted Image Dataset Interview Question
00 Fix Corrupted Image Dataset Interview Question Overview
01 Fix Corrupted Dataset (6:58)
02 Count Number Of Values In Array Column (4:14)
source files
04 Compare Smoothed Data And Original
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