Introduction

The year 2023 witnessed groundbreaking advancements in Natural Language Processing (NLP) with the rise of powerful language models like Bard, Gemini, and ChatGPT. These wonders are not just impressive feats of AI evolution – they signify the dawn of a new era where machines can understand and generate human language with unprecedented accuracy and fluency. From personalized chatbots to real-time translation, NLP is revolutionizing the way we interact with technology and each other. As these applications become more pervasive, mastering NLP will no longer be a skill but a necessity. 

Keeping this in mind, we have created a six-month, step-by-step learning path to become an NLP expert in 2024. This NLP learning path will begin with the prerequisites you need to know beforehand. Thereafter, we will guide you month-on-month, telling you exactly what you need to learn and practice to become an NLP expert.

So, let’s begin!

Comprehensive NLP Learning Path Overview 2024

Are you curious about Natural Language Processing (NLP)? Then this learning path is for you! It’s designed to help you learn the basics of NLP in just 6 months, even if you’re a beginner.

What will you learn?

• Month 1: Get started with Python and basic machine learning. Learn about statistics, probability, and deep learning concepts for NLP.
• Month 2 & 3: Master text processing techniques, word embeddings, and deep learning frameworks like PyTorch and TensorFlow. Build your first projects in text summarization and machine translation.
• Month 4 & 5: Discover powerful pre-trained models like BERT and GPT-3. Learn transfer learning, prompt engineering, and fine-tuning techniques. Build applications with large language models.
• Month 6: Take your skills to the next level by creating your own language model. Become an NLP expert!

Why choose this path?

• Easy to follow: This path is designed for beginners, with clear instructions and projects.
• Hands-on learning: You’ll learn by doing, with practical projects that build your skills.
• Become an expert: By the end of this path, you’ll have the skills to build your own NLP applications.

Pre-requisites

Before embarking on this NLP learning path, it’s essential to have a solid foundation in the following areas:

• Python: Familiarize yourself with the Python programming language, as it is widely used in NLP libraries and frameworks.
• Basic Machine Learning Algorithms: Gain a basic understanding of machine learning algorithms such as Logistic Regression, Decision Trees, K-Nearest Neighbors, and Naive Bayes.
• Basic Deep Learning Concepts: Familiarize yourself with the fundamental concepts of deep learning, including neural networks and their training processes.
• Mathematics: Brush up on your knowledge of statistics and probability, as they form the backbone of many NLP techniques.

Quarter 1: Foundational Knowledge

In the first quarter, we will focus on fundamental NLP techniques and building the foundational knowledge of NLP. By the end of this quarter, our goal is to acquire the basic knowledge of NLP.

Month 1: Text Preprocessing and Word Embeddings

In the first month of your NLP journey, focus on the following topics:

• Text Preprocessing: Learn the foundational aspect of NLP by mastering text preprocessing techniques. This includes understanding the power of regular expressions for pattern matching, implementing stopword removal to filter out common words, and exploring stemming and lemmatization for reducing words to their root forms.
• Word Embeddings: Master the concept of word embeddings, crucial for capturing semantic relationships in textual data. Gain proficiency in One Hot Encoding, a basic representation; TF-IDF, a method considering term importance; Word2Vec, which learns word vectors; and FastText, incorporating sub-word information.

Projects

• Sentiment Analysis: Apply your acquired skills to conduct sentiment analysis on textual data. Utilize text preprocessing methods and diverse word embedding techniques to understand and classify sentiments, laying the foundation for more advanced NLP applications.
• Fake News Detection: Demonstrate the practical application of NLP in real-world scenarios. Build a project focused on detecting fake news by employing text preprocessing and word embeddings to unveil patterns and linguistic cues indicative of misinformation.

Research Papers

• TF-IDF: Dive deeper into the research paper on Term Frequency-Inverse Document Frequency (TF-IDF) and understand its significance in NLP.
• Word2Vec: Explore the research paper on Word2Vec, a popular word embedding technique.

Month 2: Deep Learning NLP and Text Summarization

In the second month, delve into the world of deep learning and its applications in NLP:

• Deep Learning NLP Frameworks: Immerse yourself in the powerful landscape of deep learning with a focus on frameworks like PyTorch and TensorFlow. Gain hands-on experience to leverage their capabilities in solving complex NLP challenges.
• NLP Techniques: Explore a spectrum of advanced NLP techniques, including Convolutional Neural Networks (CNN) for feature extraction, Recurrent Neural Networks (RNN) for sequential data, Long Short-Term Memory (LSTM) networks for handling long-range dependencies, Gated Recurrent Unit (GRU) for efficient training, and Encoder-Decoder models for tasks like sequence-to-sequence learning.

Projects

• Text Summarization: Apply your knowledge of deep learning NLP techniques to create a system that automatically generates concise summaries from lengthy texts. This project sharpens your skills in understanding and representing meaningful content.
• Machine Translation: Explore multilingual communication by developing a machine translation project. Utilize deep learning models to translate text seamlessly between languages, showcasing the transformative power of NLP in bridging linguistic gaps.

Research Papers

• CNN , RNN: Explore the research paper on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in the context of NLP.
• LSTM , Encoder-decoder: Dive deeper into the research paper on Long Short-Term Memory (LSTM) and Encoder-Decoder architecture.

Month 3: Attention Mechanisms and Transfer Learning

In the third month, focus on attention mechanisms and transfer learning in NLP:

• Attention is All You Need: Delve into the groundbreaking research paper, “Attention is All You Need,” to unravel the transformative role of attention mechanisms in NLP tasks. Grasp the fundamental concepts behind attention and its application in enhancing model performance.
• Transformer-Based Models: Explore the realm of state-of-the-art transformer-based models like BERT, Roberta, and GPT-1-2. Understand how these pre-trained models have reshaped the landscape of NLP through their ability to capture intricate contextual relationships in language.

Projects

• Next Word Prediction: Apply your newfound knowledge of attention mechanisms to develop a project focused on predicting the next word in a given sentence. This hands-on endeavor will sharpen your skills in implementing attention-based strategies, providing valuable insights into language modeling and contextual understanding.

Research Papers

• Attention Paper: Dive deeper into the research paper on attention mechanisms in transformer models. This single research paper introduces a lot of crucial concepts.

Quarter 2: Building LLMs from Scratch

By the end of quarter 1, you will have the solid foundational knowledge required for NLP. There is a list of projects you can do to strengthen your knowledge further. I will leave a link to these projects in the description below. Now, in Quarter 2, comes the more hands-on part. Here, we will look closely at LLMs and how to train, fine-tune, and build them. Our goal in quarter 2 is to know how to fine-tune and also make a LLM from scratch.

Month 4: Leveraging Language Models and Prompt Engineering

In the fourth month, learn how to leverage language models and engineer prompts for better NLP performance:

• Get started with LLMs: Begin your exploration of Language Models (LLMs) by understanding different types, such as Base models and those tailored for specific tasks. Learn about language representation and task adaptation.
• Foundation Models: Explore pivotal models like GPT (Generative Pre-trained Transformer), PaLM (Pattern Learning Model), and Llama. Understand the architectural foundations and capabilities that make these models integral to advancing NLP applications.

Projects

• Building LLM Apps using RAG: Apply your knowledge by developing applications that leverage Retrieval-Augmented Generation (RAG) techniques. Harness the power of prompt engineering and retrieval mechanisms to enhance language generation, creating applications that demonstrate the practical impact of advanced language models.

Month 5: Fine-tuning Foundation Models and Advanced Techniques

In the fifth month, concentrate on fine-tuning foundation models and advanced techniques. Delve into the intricacies of fine-tuning language models, exploring advanced techniques such as Prompt Engineering Fine-Tuning (PEFT) and Lora-Qlora. Gain an understanding of how these methods can significantly enhance the adaptability of foundation models for specific NLP tasks.

Projects

Finetune LLM Model: Apply your knowledge of fine-tuning techniques by undertaking a project that involves refining a foundation language model for a particular NLP task. This hands-on experience will deepen your understanding of model adaptation and optimization, crucial for tailoring language models to specific applications.

Also Read: Beginners’ Guide to Finetuning Large Language Models (LLMs)

Month 6: Training LLMs from Scratch and Building Custom Models

In the final month of your NLP learning path, explore the process of training language models from scratch and building your custom models:

Projects

Building LLM Models: Conclude your NLP journey by taking on a challenging project—train a custom language model from scratch, akin to creating Llama 2, tailored for a specific NLP task. This endeavor will showcase your proficiency in model architecture design, training methodologies, and the ability to address task-specific nuances, marking a significant milestone in your mastery of natural language processing.

Also Read: Beginner’s Guide to Build Your Own Large Language Models from Scratch

Summing Up

Congratulations on completing this comprehensive 6-month NLP learning path to become an NLP Expert in 2024.

At Analytics Vidhya, we’ve empowered over ~400k data science enthusiasts with industry-focused career roadmaps. If you aspire to become an NLP Expert without leaving your job, consider enrolling in our GenAI Pinnacle program. This exclusive program offers a personalized learning roadmap, 200+ hours of immersive learning, 10+ real-world projects, weekly 1:1 mentorship with Generative AI experts, and mastery of 26+ GenAI tools and libraries.

Your structured journey has equipped you with essential skills, hands-on projects, and research exploration. Remember, continuous learning is key to enhancing your expertise in this dynamic NLP field. Happy NLP exploration!

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