Algorithms_List.txt

Must Know How to Implement:

1.Linear Regression
2.Logistic Regression
3.k-Nearest Neighbors (KNN)
4.Naive Bayes
5.Support Vector Machine (Linear)
6.Decision Tree
7.Random Forest
8.K-Means Clustering
9.Principal Component Analysis (PCA)
10.Multilayer Perceptron (MLP)
11.Convolutional Neural Network (CNN)
12.Recurrent Neural Network (RNN)
13.Long Short-Term Memory (LSTM)
Optional: (ResNet)

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Must Be Aware of the Theory:

1.Kernel Support Vector Machine
2.Gradient Boosting Machines
3.AdaBoost
4.Hierarchical Clustering
5.DBSCAN
6.t-SNE
7.UMAP
8.Batch Normalization
9.Dropout
10.Autoencoder
11.Variational Autoencoder (VAE)
12.Generative Adversarial Network (GAN)
13.Transformer
14.BERT
15.GPT
16.Markov Decision Process (MDP)
17.Q-Learning
18.Deep Q-Network (DQN)


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