Welcome
1.
Fundamental Machine Learning Algorithms
1.1.
K-Nearest Neighbours
1.2.
Linear Regression
1.3.
Logistic Regression
1.4.
Softmax Regression
1.5.
Perceptron
1.6.
Naive Bayes Classifier
1.7.
Support Vector Machine
1.8.
Decision Tree Classifier
1.9.
Random Forest Classifier
1.10.
K-Means Clustering
1.11.
AdaBoost
1.12.
Principal Component Analysis
1.13.
Linear Discriminant Analysis
2.
Neural Networks & Deep Learning
2.1.
Tensor Manipulation
2.2.
Autograd & Computational Graphs
2.3.
Backpropagation
2.4.
Gradient Descent
2.5.
Training Loop
2.6.
Linear Regression Revisited
2.7.
Logistic Regression Revisited
2.8.
Softmax & Cross-Entropy Loss Revisited
2.9.
Activation Functions
2.10.
DataLoaders & Transforms
2.11.
Neural Networks
2.12.
Convolutional Neural Networks
2.13.
Transfer Learning
2.14.
Transformers & Paper Replicating
3.
Data Handling & Model Deployment
3.1.
Handling Data
3.2.
Visualising Data
3.3.
Tensorboard
3.4.
Saving & Loading Models
3.5.
Model Deployment
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Machine Learning Fundamentals