Enroll Course: https://www.udemy.com/course/natural-language-processing-in-python-korean/
Have you ever wondered how AI technologies like ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion actually work? If so, the Udemy course “【AI 자막】 머신 러닝: Python에서의 자연어 처리 마스터하기! (V2)” is your gateway to understanding the foundational principles behind these groundbreaking applications.
This comprehensive 4-in-1 course, featuring AI-generated Korean subtitles, offers an in-depth exploration of Natural Language Processing (NLP) using Python. It’s structured into four key modules, each building upon the last to provide a robust understanding of NLP techniques.
**Part 1: Vector Models and Text Preprocessing**
This section lays the groundwork by explaining the critical role of vectors in data science and AI. You’ll learn various methods for converting text into vectors, including CountVectorizer and TF-IDF. Furthermore, the course delves into neural embedding techniques like word2vec and GloVe. The practical applications covered include text classification, document retrieval, and text summarization. Essential text preprocessing steps like tokenization, stemming, and lemmatization are also thoroughly explained, along with an introduction to classic NLP tasks such as Part-of-Speech tagging.
**Part 2: Probabilistic Models and Markov Models**
Here, you’ll dive into probabilistic models, a cornerstone of data science and machine learning for the past century, with applications extending beyond NLP to finance, bioinformatics, and reinforcement learning. The course demonstrates how to use these models for building text classifiers, article spinning, and even generating poetry. Crucially, understanding these models is presented as a prerequisite for grasping the workings of modern Transformer models like BERT and GPT-3, with a focus on pre-training objectives relevant to these advanced architectures.
**Part 3: Machine Learning Methods**
This module focuses on applying classic machine learning algorithms to common NLP tasks such as spam detection, sentiment analysis, Latent Semantic Analysis, and topic modeling. The emphasis is on practical application rather than just theoretical details. You’ll gain hands-on experience with algorithms like Naive Bayes, Logistic Regression, PCA/SVD, and Latent Dirichlet Allocation (LDA), all of which are fundamental to NLP.
**Part 4: Deep Learning Methods**
The final section explores state-of-the-art neural network architectures for NLP. You’ll learn about Feedforward Neural Networks (ANNs), embeddings, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). The RNN section specifically covers advanced architectures like LSTMs and GRUs, widely used by tech giants for tasks like language translation and speech recognition. As Transformer models are a prime example of deep neural networks, this course positions itself as essential preparation for understanding them.
**Key Strengths of the Course:**
* **Detailed Explanations:** Every line of code is meticulously explained.
* **Efficiency:** No time wasted on unnecessary “typing” demonstrations; the focus is on learning.
* **Mathematical Depth:** The course doesn’t shy away from the mathematical underpinnings of algorithms, providing insights often missed in other courses.
This course is highly recommended for anyone looking to build a strong foundation in Natural Language Processing and understand the mechanics behind today’s most exciting AI advancements. It’s a practical, in-depth learning experience that equips you with the knowledge and skills to tackle complex NLP challenges.
Enroll Course: https://www.udemy.com/course/natural-language-processing-in-python-korean/