Course Content
1.
Accuracy Score
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2 min
0
2.
Activation Function
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2 min
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3.
Algorithm
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2 min
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4.
Assignment Operator (Python)
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2 min
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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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8.
Artificial Neural Network (ANN)
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2 min
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9.
Backpropagation
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2 min
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10.
Bias
0 min
2 min
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11.
Bias-Variance Tradeoff
0 min
2 min
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12.
Big Data
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2 min
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13.
Business Analyst (BA)
0 min
2 min
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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
0 min
1 min
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19.
Computer Vision
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2 min
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20.
Continuous Variable
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1 min
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21.
Cost Function
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2 min
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22.
Cross-Validation
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2 min
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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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2 min
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32.
Gradient Descent
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2 min
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33.
Hidden Layer
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2 min
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34.
Hyperparameter
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2 min
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35.
Image Recognition
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2 min
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36.
Imputation
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2 min
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37.
K-fold Cross Validation
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2 min
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38.
K-Means Clustering
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2 min
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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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44.
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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1 min
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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
0 min
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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2 min
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62.
TensorBoard
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2 min
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63.
TensorFlow
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2 min
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64.
Test Loss
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2 min
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65.
Time Series
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2 min
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66.
Time Series Data
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2 min
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67.
Test Set
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2 min
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68.
Tokenization
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2 min
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69.
Train Test Split
0 min
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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2 min
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72.
Transfer Learning
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2 min
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73.
True Negative (TN)
0 min
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
0 min
2 min
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78.
Undersampling
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2 min
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79.
Univariate Analysis
0 min
2 min
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80.
Unstructured Data
0 min
2 min
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81.
Unsupervised Learning
0 min
2 min
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82.
Validation
0 min
2 min
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83.
Validation Loss
0 min
1 min
0
84.
Vanishing Gradient Problem
0 min
2 min
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85.
Validation Set
0 min
2 min
0
86.
Variable (Python)
0 min
1 min
0
87.
Variable Importances
0 min
2 min
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88.
Variance
0 min
2 min
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89.
Variational Autoencoder (VAE)
0 min
2 min
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90.
Weight
0 min
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 False Negative is a term commonly used in binary classification problems in statistics, machine learning, and diagnostic testing. It represents an error that occurs when a test result or a classification model wrongly indicates a negative outcome when the actual situation is positive.
In the context of a binary classification:
- True Positive (TP): Represents the cases where the test result or model correctly identifies a positive condition as positive.
- False Negative (FN): Represents the cases where the test result or model incorrectly identifies a positive condition as negative.
For example, let's consider a medical test for a disease. A False Negative in this scenario would occur when the test incorrectly indicates that a person does not have the disease when, in reality, the person does have the disease.
False Negatives are particularly critical in certain applications, such as medical diagnostics or security screenings, where missing a positive case (such as a disease or a security threat) can have significant consequences. In such cases, minimizing the occurrence of False Negatives is crucial, often at the expense of increasing the occurrence of False Positives (indicating a positive outcome when it's actually negative).
The concept of False Negatives is also relevant in various other fields, such as quality control, risk assessment, and information retrieval, where the accuracy of detecting positive instances is essential.