REMOVE Introduction

In this course, we will explore the fascinating field of NLP and how it has evolved over time. We will start by diving into the history of NLP and its applications, as well as the various challenges and techniques involved in processing natural language text.

We will then move on to the topic of text pre-processing, where we will discuss how to clean and prepare text data for analysis. This includes techniques such as tokenization, stemming, and lemmatization.

Next, we will focus on building and evaluating classifiers, which are algorithms that can automatically classify text into different categories or classes. We will cover techniques such as feature engineering, supervised learning, and evaluation metrics.

After this, we will delve into the exciting world of large language models. We will discuss embeddings, which are representations of words and phrases in a high-dimensional space, and how they can be used to measure similarity between different pieces of text.

We will also explore attention, which is a mechanism that allows models to focus on specific parts of the input during processing. We will examine the transformer model architecture, which is the backbone of many state-of-the-art language models, and how it has revolutionized the field of NLP.

Finally, we will delve into semantic search, which is the process of understanding the meaning of a query and finding the most relevant results. We will discuss how large language models can be used to perform semantic search and how this technology is transforming the search industry.Throughout the course, we will use practical examples and hands-on exercises to ensure that you have a comprehensive understanding of these topics and are able to apply them in real-world scenarios.