Data Science Prerequisites

Data Science Prerequisites#

In this section, we outline the foundational skills and knowledge in data science, including key areas such as machine learning and natural language processing (NLP), required not only to complete exercises in this course but also to grasp and understand the core concepts of LLMs that will be taught. These prerequisites will provide the essential background needed to effectively work with LangChain and build LLM-based applications.

Prerequisite Skills in Data Science#

A strong foundation in data science, machine learning, and NLP is crucial for building advanced LLM-based applications. These skills will enable efficient data handling, model building, and language processing, which are fundamental for working with LLMs in real-world scenarios. Below is a list of recommended skills to help you maximize your learning in this course.

  • Data Science Basics: Familiarity with data manipulation and analysis, especially using libraries like pandas and numpy.

  • Machine Learning Fundamentals: Knowledge of core ML algorithms (e.g., linear regression, decision trees, k-nearest neighbors) and concepts such as overfitting, training/testing splits, and evaluation metrics.

  • Deep Learning Basics: Basic understanding of neural networks, including feedforward networks and concepts like activation functions, training, and backpropagation.

  • Natural Language Processing (NLP) Basics: Familiarity with NLP concepts such as tokenization, word embeddings, and basic text processing techniques.

  • Working with ML Frameworks: Experience with libraries like scikit-learn for traditional ML models and TensorFlow or PyTorch for deep learning.

Youtube Videos#

If you are very, very short on time and just want a quick introduction to LLMs, please watch this video:

30 Minute Introduction to Large Language Models

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