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ANN (Fwd, Bwd)_Manual:
Consists of step by step working of Neural Networks performed on a randomly generated dataset, namely defining the layers, Forward and Backward Propogation and also updating the parameters during Back propogation -
ANN_Hidden_layers:
All steps namely defining the layers, Forward and Backward Propogation are performed on the MNIST dataset by creating functions for taking inputs and calculating cost automatically rather than hard coding for each layer. It also visualizes the Hidden layers giving a visualization of how the model learns.
- Linear Regression using Gradient Descent
- Logistic Regression using Sigmoid
- K-Nearest Neighbour - Supervised Learning
- Naive Bayes - Probabilistic Classifier
- Principal Component Analysis - Dimension Reduction
- Dimension reduction:
- Principal Component Analysis
- Classification Algorithms:
- Naive Bayes algorithm
- K-Nearest Neigbour
- Logistic Regression
- Support Vector Machine
- Tree Based Algorithms:
- Decision Tree Classifier
- Random Forest Classifier
- Boostong Techniques:
- AdaBoost
- Gradient Boosting
- Voting Classifier:
- Compares all the Classification algorithms mentioned above.