Course Content
1.
Accuracy Score
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2 min
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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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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
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2 min
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11.
Bias-Variance Tradeoff
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2 min
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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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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
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1 min
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19.
Computer Vision
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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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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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Gradient Descent
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Hidden Layer
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Hyperparameter
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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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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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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
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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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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
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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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2 min
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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
0 min
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
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2 min
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86.
Variable (Python)
0 min
1 min
0
87.
Variable Importances
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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
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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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In machine learning, the "target" refers to the variable that the model aims to predict or estimate based on the input data. It is also known as the dependent variable, response variable, outcome variable, or label, depending on the context and terminology used in different fields.
The target variable represents the output or outcome of interest in a supervised learning task. In supervised learning, the machine learning algorithm learns from the input data (features or independent variables) to make predictions or classifications about the target variable.
For example:
- In a regression problem, the target variable is continuous, such as predicting house prices, where the target variable might represent the price of a house based on various features like size, location, etc.
- In a classification problem, the target variable is categorical, such as classifying emails as spam or not spam based on various features extracted from the email content.
The primary goal in supervised learning is to build a predictive model that can accurately predict or classify the target variable using the patterns and relationships present in the input data. The quality of the predictions or classifications is evaluated based on how well the model performs in estimating or classifying the target variable on unseen data.