Introduction to Basics LLM Models

Explore LLMs with hands-on training in NLP, neural networks, clustering, classification, and model evaluation.

Description

This comprehensive course offers an in-depth introduction to Large Language Models (LLMs) and foundational machine learning concepts through practical, hands-on learning. Designed for learners at the beginner to intermediate level, the course bridges the gap between theory and real-world application by exploring the architecture, training, and capabilities of LLMs like GPT and BERT. You will learn how these models revolutionize natural language processing (NLP) tasks such as text generation, translation, summarization, and sentiment analysis.

The course also covers key machine learning algorithms including logistic regression, decision trees, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and clustering techniques like K-Means, DBSCAN, and hierarchical clustering. You will gain a deep understanding of model evaluation metrics, such as accuracy, precision, recall, F1-score, and silhouette scores, as well as how to handle challenges like imbalanced datasets using techniques such as SMOTE.

In the neural networks module, you’ll explore the core concepts behind activation functions, backpropagation, overfitting, and regularization. Using Python and popular libraries like scikit-learn, pandas, and matplotlib, you’ll build and train models, interpret outputs, and evaluate performance.

By the end of the course, you’ll have developed a practical skill set to build, train, and interpret models across various domains, preparing you to apply machine learning and LLMs to real-world problems in business, healthcare, finance, and beyond.

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