Introduction to Basics LLM Models

Explore the assumptions of linear regression, including linearity, independence, normality, homoscedasticity, and no multicollinearity, and learn to verify them for reliable models, with an introduction to LLMs.

Learn how large language models use deep learning and fine tuning, then fit a linear regression to predict a dependent variable from independent variables using intercept and slope.

Evaluate the linear regression model using practical metrics, code snippets, and examples, and explore how large language models inform data analysis and prediction tasks.

Explore large language models in natural language processing and their applications in translation, text generation, and sentiment analysis. Understand logistic regression for binary classification, training, and coefficient interpretation.

Learn how large language models enable natural language processing tasks such as text generation, translation, and question answering, and interpret logistic regression coefficients to gauge feature influence in binary classification.

Explore large language models and their applications, then learn to build a decision tree model from scratch with hands-on steps: data preprocessing, feature selection, training, evaluation, and deployment.

Explore the fundamentals of large language models and hands-on pruning of decision trees. Learn pre-pruning and post-pruning techniques to improve generalization and interpretability.

Explore the fundamentals of large language models (LLMs) and examine the advantages and disadvantages of decision trees, a widely used, interpretable machine learning algorithm for classification, regression, and decision making.

Explore llms and the fundamentals of the random forest algorithm, handling numerical, categorical, and text data, with interpretable models for classification, regression, and anomaly detection.

Explore how large language models relate to random forests and learn the ensemble of decision trees, its data preprocessing, model evaluation, and Python scikit-learn implementation.

Explore the fundamentals of large language models and tune random forest hyperparameters with practical grid search techniques in Python using scikit-learn.

Explore large language models and the basics of random forest, and learn how to measure, visualize, and interpret feature importance to improve model performance.

Explore the fundamentals of large language models and hands-on support vector machines with Python and scikit-learn, covering data preprocessing, training, evaluation, and hyperparameter effects.

Explore kernel functions in SVM to map data into higher dimensions for a separating hyperplane, and examine polynomial and rbf kernels in classifications, along with large language models and attention.

Explore the fundamentals of large language models and apply SVM to classification, covering data preprocessing, feature engineering, model training, evaluation, and task-specific fine-tuning.

Explore large language models and the k-nearest neighbors algorithm, a non-parametric method for classification and regression, with distance-based prediction and k as a hyperparameter.

Explore how the k value in large language models controls the number of tokens considered, shaping coherence and diversity for chatbots, translation, and creative writing.

Explore distance metrics in k-nearest neighbors for classification and regression, including Euclidean, Manhattan, and cosine similarity. Understand how normalization affects these metrics in KNN.

Explore large language models and address imbalanced data in the knn algorithm using oversampling with smote, undersampling, and ensemble methods like bagging, boosting, and stacking.

Explore large language models and hands-on k-means clustering, an unsupervised algorithm. Preprocess data, initialize k centers, assign and update points, converge; use elbow method and silhouette score to reveal segments.

Explore hierarchical clustering and its use with diverse data types to segment customers, interpret dendrograms, and compare single, complete, and Ward linkages using Python and scikit-learn.

Explore the dbscan clustering algorithm and llms in an engaging hands-on demonstration, highlighting density-based clustering, core points, robustness to noise, and unsupervised learning for real-world data tasks.

Explore cluster evaluation metrics for large language models, including the silhouette score and the kulinski index, to assess and improve text clustering for topics, classification, and content organization.

Explore the fundamentals of neural networks and large language models, including feedforward, convolutional, and recurrent architectures, with a hands-on Python project using TensorFlow or PyTorch to train and evaluate.

Explore activation functions in neural networks and impact on learning and convergence, highlighting sigmoid, tanh, relu, leaky relu, softmax, and variants like swish and mesh for binary and multi-class tasks.

Explore the fundamentals of large language models and hands-on neural network training, including backpropagation, hyperparameters, and practical use with TensorFlow or PyTorch.

Explore overfitting and regularization in neural networks and llms. Learn how L2 regularization, or weight decay, adds penalties to the loss function to improve generalization on unseen data.