Course Content
1.
Accuracy Score
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2 min
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2.
Activation Function
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3.
Algorithm
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2 min
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4.
Assignment Operator (Python)
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5.
Artificial General Intelligence (AGI)
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3 min
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6.
Artificial Intelligence
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4 min
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7.
Artificial Narrow Intelligence (ANI)
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3 min
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Artificial Neural Network (ANN)
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2 min
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9.
Backpropagation
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Bias
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11.
Bias-Variance Tradeoff
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12.
Big Data
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2 min
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13.
Business Analyst (BA)
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14.
Business Analytics (BA)
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2 min
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15.
Business Intelligence (BI)
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1 min
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16.
Categorical Variable
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1 min
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17.
Clustering
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2 min
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18.
Command Line
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1 min
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19.
Computer Vision
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20.
Continuous Variable
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1 min
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21.
Cost Function
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Cross-Validation
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23.
Data Analysis
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7 min
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24.
Data Analyst
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4 min
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25.
Data Science
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1 min
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26.
Data Scientist
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6 min
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27.
Early Stopping
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2 min
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28.
Exploratory Data Analysis (EDA)
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2 min
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29.
False Negative
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1 min
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30.
False Positive
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1 min
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31.
Google Colaboratory
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32.
Gradient Descent
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Hidden Layer
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34.
Hyperparameter
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35.
Image Recognition
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36.
Imputation
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37.
K-fold Cross Validation
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38.
K-Means Clustering
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39.
Linear Regression
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2 min
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40.
Logistic Regression
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1 min
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41.
Machine Learning Engineer (MLE)
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5 min
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42.
Mean
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2 min
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43.
Neural Network
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2 min
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Notebook
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3 min
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45.
One-Hot Encoding
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2 min
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46.
Operand
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1 min
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47.
Operator (Python)
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1 min
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48.
Print Function (Python)
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49.
Python
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5 min
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50.
Quantile
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1 min
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51.
Quartile
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1 min
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52.
Random Forest
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2 min
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53.
Recall
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2 min
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54.
Scalar
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2 min
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55.
Snake Case
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1 min
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56.
T-distribution
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2 min
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57.
T-test
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2 min
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58.
Tableau
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2 min
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59.
Target
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1 min
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60.
Tensor
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2 min
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61.
Tensor Processing Unit (TPU)
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62.
TensorBoard
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63.
TensorFlow
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64.
Test Loss
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2 min
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65.
Time Series
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66.
Time Series Data
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67.
Test Set
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68.
Tokenization
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69.
Train Test Split
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2 min
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70.
Training Loss
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2 min
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71.
Training Set
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72.
Transfer Learning
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2 min
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73.
True Negative (TN)
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1 min
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74.
True Positive (TP)
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1 min
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75.
Type I Error
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2 min
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76.
Type II Error
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2 min
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77.
Underfitting
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2 min
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78.
Undersampling
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2 min
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79.
Univariate Analysis
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2 min
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80.
Unstructured Data
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2 min
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81.
Unsupervised Learning
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2 min
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82.
Validation
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2 min
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83.
Validation Loss
0 min
1 min
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84.
Vanishing Gradient Problem
0 min
2 min
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85.
Validation Set
0 min
2 min
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86.
Variable (Python)
0 min
1 min
0
87.
Variable Importances
0 min
2 min
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88.
Variance
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2 min
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89.
Variational Autoencoder (VAE)
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2 min
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90.
Weight
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1 min
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91.
Word Embedding
0 min
2 min
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92.
X Variable
0 min
2 min
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93.
Y Variable
0 min
2 min
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94.
Z-Score
0 min
1 min
0
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A categorical variable is a type of variable in statistics that represents categories or groups. Unlike numerical variables that represent measurable quantities, categorical variables are used to label or categorize data into distinct groups or classes. Categorical variables can be further divided into two main types: nominal and ordinal.
Nominal Categorical Variable:
- Nominal variables represent categories with no inherent order or ranking.
- Examples include gender (male, female), colors (red, blue, green), or types of fruits (apple, orange, banana).
- Nominal variables are often used to classify or group observations without implying any specific order.
Ordinal Categorical Variable:
- Ordinal variables represent categories with a meaningful order or ranking.
- While the categories have an order, the intervals between them may not be uniform or measurable.
- Examples include education levels (high school, college, graduate), customer satisfaction ratings (poor, fair, good, excellent), or socioeconomic status (low, middle, high).
- Ordinal variables provide information about the relative position or ranking of the categories, but the differences between them may not be precisely defined.
Categorical variables are often represented using labels or names, and statistical analyses involving categorical variables may include techniques like contingency tables, chi-square tests, or logistic regression, depending on the nature of the data and research questions.