Machine Learning
Machine learning is a branch of Artifcial Intelligence (AI) where computers learn patterns from data and improve their performance over time without being explicitly programmed.
Machine Learning
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Learning Styles
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Supervised Learning
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Classification
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Use Case
- Identity Fraud Detection
- Image Classification
- Customer Retention
- Diagnostics
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Binary Classification Evaluation Metrics
- Accuracy (TN+TP) ÷ (TN+FN+FP+TP)
- Recall TP ÷ (TP+FN)
- Precision TP ÷ (TP+FP)
- F1-score (2 x Precision x Recall) ÷ (Precision + Recall)
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Area Under the Curve (AUC)
- true positive rate (TPR) TP ÷ (TP+FN)
- false positive rate (FPR) FP ÷ (FP+TN)
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Use Case
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Regression
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Use Case
- Advertising Popularity Prediction
- Weather Forecasting
- Market Forecasting
- Estimating Life Expectancy
- Population Growth Prediction
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Regression Evaluation Metrics
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- Root Mean Squared Error (RMSE)
- Coefficient of Determination (R2)
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Use Case
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Classification
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Unsupervised Learning
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Clustering
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Use Case
- Reommender Systems
- Targetted Marketing
- Customer Segmentation
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Use Case
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Dimensionality Reduction
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Use Case
- Big Data Visualisation
- Meaningful Compression
- Structure Discovery
- Feature Elicitation
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Use Case
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Clustering
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Reinforcement Learning
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Use Case
- Real time Decisions
- Robot Navigation
- Learning Tasks
- Skills Acquisition
- Game AI
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Use Case
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Supervised Learning
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Classifer
- Logistic Regression
- Support Vector Machines SVM
- Naive Bayes
- Neural Networks
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Neural Network
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Resttricted Boltzmann Machine
- Feature Extraction
- Pattern Recognition
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Autoencoder
- Feature Extraction
- Pattern Recognition
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Recursive Neural Tensor Network
- Text Processing
- Object Recognition
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Recurrent Network
- Text Processing
- Speech Recognition
- Time Series Analysis
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Deep Belief Network
- Image Recognition
- Classification
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Convolutional Network
- Image Recognition
- Object Recognition
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Resttricted Boltzmann Machine