- Machine Learning Basics
- Machine Learning - Home
- Machine Learning - Getting Started
- Machine Learning - Basic Concepts
- Machine Learning - Python Libraries
- Machine Learning - Applications
- Machine Learning - Life Cycle
- Machine Learning - Required Skills
- Machine Learning - Implementation
- Machine Learning - Challenges & Common Issues
- Machine Learning - Limitations
- Machine Learning - Reallife Examples
- Machine Learning - Data Structure
- Machine Learning - Mathematics
- Machine Learning - Artificial Intelligence
- Machine Learning - Neural Networks
- Machine Learning - Deep Learning
- Machine Learning - Getting Datasets
- Machine Learning - Categorical Data
- Machine Learning - Data Loading
- Machine Learning - Data Understanding
- Machine Learning - Data Preparation
- Machine Learning - Models
- Machine Learning - Supervised
- Machine Learning - Unsupervised
- Machine Learning - Semi-supervised
- Machine Learning - Reinforcement
- Machine Learning - Supervised vs. Unsupervised
- Machine Learning Data Visualization
- Machine Learning - Data Visualization
- Machine Learning - Histograms
- Machine Learning - Density Plots
- Machine Learning - Box and Whisker Plots
- Machine Learning - Correlation Matrix Plots
- Machine Learning - Scatter Matrix Plots
- Statistics for Machine Learning
- Machine Learning - Statistics
- Machine Learning - Mean, Median, Mode
- Machine Learning - Standard Deviation
- Machine Learning - Percentiles
- Machine Learning - Data Distribution
- Machine Learning - Skewness and Kurtosis
- Machine Learning - Bias and Variance
- Machine Learning - Hypothesis
- Regression Analysis In ML
- Machine Learning - Regression Analysis
- Machine Learning - Linear Regression
- Machine Learning - Simple Linear Regression
- Machine Learning - Multiple Linear Regression
- Machine Learning - Polynomial Regression
- Classification Algorithms In ML
- Machine Learning - Classification Algorithms
- Machine Learning - Logistic Regression
- Machine Learning - K-Nearest Neighbors (KNN)
- Machine Learning - Naïve Bayes Algorithm
- Machine Learning - Decision Tree Algorithm
- Machine Learning - Support Vector Machine
- Machine Learning - Random Forest
- Machine Learning - Confusion Matrix
- Machine Learning - Stochastic Gradient Descent
- Clustering Algorithms In ML
- Machine Learning - Clustering Algorithms
- Machine Learning - Centroid-Based Clustering
- Machine Learning - K-Means Clustering
- Machine Learning - K-Medoids Clustering
- Machine Learning - Mean-Shift Clustering
- Machine Learning - Hierarchical Clustering
- Machine Learning - Density-Based Clustering
- Machine Learning - DBSCAN Clustering
- Machine Learning - OPTICS Clustering
- Machine Learning - HDBSCAN Clustering
- Machine Learning - BIRCH Clustering
- Machine Learning - Affinity Propagation
- Machine Learning - Distribution-Based Clustering
- Machine Learning - Agglomerative Clustering
- Dimensionality Reduction In ML
- Machine Learning - Dimensionality Reduction
- Machine Learning - Feature Selection
- Machine Learning - Feature Extraction
- Machine Learning - Backward Elimination
- Machine Learning - Forward Feature Construction
- Machine Learning - High Correlation Filter
- Machine Learning - Low Variance Filter
- Machine Learning - Missing Values Ratio
- Machine Learning - Principal Component Analysis
- Machine Learning Miscellaneous
- Machine Learning - Performance Metrics
- Machine Learning - Automatic Workflows
- Machine Learning - Boost Model Performance
- Machine Learning - Gradient Boosting
- Machine Learning - Bootstrap Aggregation (Bagging)
- Machine Learning - Cross Validation
- Machine Learning - AUC-ROC Curve
- Machine Learning - Grid Search
- Machine Learning - Data Scaling
- Machine Learning - Train and Test
- Machine Learning - Association Rules
- Machine Learning - Apriori Algorithm
- Machine Learning - Gaussian Discriminant Analysis
- Machine Learning - Cost Function
- Machine Learning - Bayes Theorem
- Machine Learning - Precision and Recall
- Machine Learning - Adversarial
- Machine Learning - Stacking
- Machine Learning - Epoch
- Machine Learning - Perceptron
- Machine Learning - Regularization
- Machine Learning - Overfitting
- Machine Learning - P-value
- Machine Learning - Entropy
- Machine Learning - MLOps
- Machine Learning - Data Leakage
- Machine Learning - Resources
- Machine Learning - Quick Guide
- Machine Learning - Useful Resources
- Machine Learning - Discussion
Machine Learning - Supervised vs. Unsupervised
Machine Learning approaches can be either Supervised or Unsupervised. If you can anticipate the expanse of data, and if it is possible to divide the data into categories, then the best approach is to help the algorithm become smarter by Supervised Learning.
If you anticipate that the amount of data is massive, and if you think that the data cannot be simply classified or labelled, then it is better to go for Unsupervised Learning approach and let the algorithms handle predictions smartly.
Differences between Supervised and Unsupervised Machine Learning
The table below shows some key differences between supervised and unsupervised machine learning −
| Supervised Technique | Unsupervised Technique |
|---|---|
| Supervised machine learning algorithms are trained using both training data and its associated output i.e., label data. | Unsupervised machine learning algorithms do not require labeled data for training. |
| Supervised machine learning model learns the association between input training data and their labels. | Unsupervised machine learning model learns the pattern and relationship from the given raw data. |
| Supervised ML model takes feedback to check whether it is predicting the correct output or not. | Unsupervised ML model does not take any kind of feedback. |
| As name entails, supervised machine learning algorithms needs supervision to train the model. | As name entails, unsupervised machine learning algorithms does not any kind of supervision to train the model. |
| We can divide supervised machine learning algorithms in two broad classes namely Classification and Regression. | Clustering, Anomaly Detection, Association, and Association are some of the broad classed of unsupervised machine learning algorithms. |
| In terms of computational complexity, supervised machine learning methods are computationally simple. | Unsupervised machine learning methods are computationally complex. |
| Supervised machine learning methods are highly accurate. | Unsupervised machine learning methods are less accurate. |
| In supervised machine learning, the learning takes place offline. | In unsupervised machine learning, the learning takes place in real time. |
| Number of classes is already known before implementing supervised machine learning methods. | In unsupervised learning methods, number of classes are not known in prior. |
| One of the main drawbacks of supervised learning is to classify big data. | As the data used in unsupervised learning is not labeled, getting precise information regarding data sorting is one of the main drawbacks of it. |
| Some of the well-known supervised machine learning algorithms are KNN (k-nearest neighbors), Decision tree, Logistic Regression, and Random Forest. | Some of the well-known unsupervised machine learning algorithms are Hebbian Learning, K-means Clustering, and Hierarchical Clustering. |
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