Added course outline

src/SUMMARY.md

@@ -4,3 +4,33 @@
 
 - [K-Nearest Neighbours](./machine-learning/knn.md)
 - [Linear Regression](./machine-learning/linear_regression.md)
+- [Logistic Regression](home.md)
+- [Naive Bayes Classifier](home.md)
+- [Perceptron](home.md)
+- [Support Vector Machine](home.md)
+- [Decision Tree Classifier](home.md)
+- [Random Forest Classifier](home.md)
+- [K-Means Clustering](home.md)
+- [AdaBoost](home.md)
+- [Linear Discriminant Analysis](home.md)
+- [Handling Data](home.md)
+- [Visualising Data](home.md)
+
+- [Tensor Manipulation](home.md)
+- [Autograd & Computational Graphs](home.md)
+- [Backpropagation](home.md)
+- [Gradient Descent](home.md)
+- [Training Loop](home.md)
+- [Linear Regression](home.md)
+- [Logistic Regression](home.md)
+- [Handling Data](home.md)
+- [Softmax & Cross-Entropy Loss](home.md)
+- [Activation Functions](home.md)
+- [Neural Networks](home.md)
+- [CNN](home.md)
+- [Transfer Learning](home.md)
+- [Transformers & Paper Replicating](home.md)
+
+- [Tensorboard](home.md)
+- [Saving & Loading Models](home.md)
+- [Model Deployment](home.md)