In this comprehensive course, you will learn how to navigate the essentials of Natural Language Processing (NLP) and develop skills in text preprocessing. By the end of the course, you will be well-versed in NLP terminology, vector models, and various techniques for processing textual data. This course is designed to help you understand how to transform raw text into a usable format for machine learning tasks.

The journey begins with an introduction to NLP, where you will explore basic definitions, followed by an in-depth look into the Bag of Words model and Count Vectorizer theory. You’ll also engage in hands-on exercises with code implementations, such as applying Count Vectorizer and TF-IDF to text data. Additionally, the course dives into tokenization, stopwords, stemming, and lemmatization, equipping you with the fundamental tools for any NLP project. As you progress, you'll be introduced to more advanced concepts like vector similarity and neural word embeddings. With these tools, you’ll learn how to represent and analyze text data effectively, measure the similarity between text vectors, and apply neural embeddings for deeper text comprehension. The course also emphasizes the importance of these techniques in multilingual contexts, giving you strategies to handle NLP tasks in different languages. This course is perfect for anyone eager to gain a foundational understanding of NLP and text preprocessing. It is ideal for beginners in data science and machine learning, but prior knowledge of Python and basic programming will be helpful for maximizing your learning experience. This course strikes a balance between theory and practical application, ensuring you gain valuable skills to apply in real-world NLP projects.