Learning Schedule

Let us have Leaning Plan for effective understanding as below:

Hour 1: Linear Regression

- Concept: Predict continuous values.

- Implementation: Ordinary Least Squares.

- Evaluation: R-squared, RMSE.

Hour 2: Logistic Regression

- Concept: Binary classification.

- Implementation: Sigmoid function.

- Evaluation: Confusion matrix, ROC-AUC.

Hour 3: Decision Trees

- Concept: Tree-based model for classification/regression.

- Implementation: Recursive splitting.

- Evaluation: Accuracy, Gini impurity.

Hour 4: Random Forest

- Concept: Ensemble of decision trees.

- Implementation: Bagging.

- Evaluation: Out-of-bag error, feature importance.

Hour 5: Gradient Boosting

- Concept: Sequential ensemble method.

- Implementation: Boosting.

- Evaluation: Learning rate, number of estimators.

Hour 6: Support Vector Machines (SVM)

- Concept: Classification using hyperplanes.

- Implementation: Kernel trick.

- Evaluation: Margin maximization, support vectors.

Hour 7: k-Nearest Neighbors (k-NN)

- Concept: Instance-based learning.

- Implementation: Distance metrics.

- Evaluation: k-value tuning, distance functions.

Hour 8: Naive Bayes

- Concept: Probabilistic classifier.

- Implementation: Bayes' theorem.

- Evaluation: Prior probabilities, likelihood.

Hour 9: k-Means Clustering

- Concept: Partitioning data into k clusters.

- Implementation: Centroid initialization.

- Evaluation: Inertia, silhouette score.

Hour 10: Hierarchical Clustering

- Concept: Nested clusters.

- Implementation: Agglomerative method.

- Evaluation: Dendrograms, linkage methods.

Day 11: Principal Component Analysis (PCA)

- Concept: Dimensionality reduction.

- Implementation: Eigenvectors, eigenvalues.

- Evaluation: Explained variance.

Day 12: Association Rule Learning

- Concept: Discover relationships between variables.

- Implementation: Apriori algorithm.

- Evaluation: Support, confidence, lift.

Hour 13: DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

- Concept: Density-based clustering.

- Implementation: Epsilon, min samples.

- Evaluation: Core points, noise points.

Hour 14: Linear Discriminant Analysis (LDA)

- Concept: Linear combination for classification.

- Implementation: Fisher's criterion.

- Evaluation: Class separability.

Hour15: XGBoost

- Concept: Extreme Gradient Boosting.

- Implementation: Tree boosting.

- Evaluation: Regularization, parallel processing.

Hour 16: LightGBM

- Concept: Gradient boosting framework.

- Implementation: Leaf-wise growth.

- Evaluation: Speed, accuracy.

Hour 17: CatBoost

- Concept: Gradient boosting with categorical features.

- Implementation: Ordered boosting.

- Evaluation: Handling of categorical data.

Hour 18: Neural Networks

- Concept: Layers of neurons for learning.

- Implementation: Backpropagation.

- Evaluation: Activation functions, epochs.

Hour 19: Convolutional Neural Networks (CNNs)

- Concept: Image processing.

- Implementation: Convolutions, pooling.

- Evaluation: Feature maps, filters.

Hour 20: Recurrent Neural Networks (RNNs)

- Concept: Sequential data processing.

- Implementation: Hidden states.

- Evaluation: Long-term dependencies.

Hour 21: Long Short-Term Memory (LSTM)

- Concept: Improved RNN.

- Implementation: Memory cells.

- Evaluation: Forget gates, output gates.

Hour 22: Gated Recurrent Units (GRU)

- Concept: Simplified LSTM.

- Implementation: Update gate.

- Evaluation: Performance, complexity.

Hour 23: Autoencoders

- Concept: Data compression.

- Implementation: Encoder, decoder.

- Evaluation: Reconstruction error.

Hour 24: Generative Adversarial Networks (GANs)

- Concept: Generative models.

- Implementation: Generator, discriminator.

- Evaluation: Adversarial loss.

Hour 25: Transfer Learning

- Concept: Pre-trained models.

- Implementation: Fine-tuning.

- Evaluation: Domain adaptation.

Hour 26: Reinforcement Learning

- Concept: Learning through interaction.

- Implementation: Q-learning.

- Evaluation: Reward function, policy.

Hour 27: Bayesian Networks

- Concept: Probabilistic graphical models.

- Implementation: Conditional dependencies.

- Evaluation: Inference, learning.

Hour 28: Hidden Markov Models (HMM)

- Concept: Time series analysis.

- Implementation: Transition probabilities.

- Evaluation: Viterbi algorithm.

Hour 29: Feature Selection Techniques

- Concept: Improving model performance.

- Implementation: Filter, wrapper methods.

- Evaluation: Feature importance.

Hour 30: Hyperparameter Optimization

- Concept: Model tuning.

- Implementation: Grid search, random search.

- Evaluation: Cross-validation.