PHASE C: NLP Foundation (4-6 Weeks)

This is your key area.
Focus on depth in NLP rather than jumping across unrelated AI topics.

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What to Learn First

• tokenization
• embeddings
• transformers
• BERT basics
• LLM basics
• Hugging Face ecosystem

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Hugging Face Skills (Must Learn)

• pipelines
• fine-tuning flow
• datasets loading and preprocessing
• evaluation with proper metrics

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First Research-Type Project

Project: Norwegian + Urdu AI Assistant

Start simple and build in layers:

1. sentiment analysis
2. translation
3. multilingual chatbot baseline
4. text classification module

Keep scope realistic: baseline first, then improve.

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STEP 2: Learn Research Skills

Most beginners skip this and get stuck later.

Learn:

• how to read papers
• how experiments are designed
• how evaluation is done

How to Read Papers (Beginner-Friendly)

Start with:

• Papers With Code
• Hugging Face blogs
• beginner NLP papers

Read only these sections first:

1. Abstract
2. Problem
3. Method
4. Results

That is enough initially.

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STEP 3: Build a Research Portfolio

Create and maintain:

• GitHub account
• LinkedIn profile
• Kaggle profile

Upload regularly:

• notebooks
• datasets/processed subsets
• experiment notes
• project writeups

Consistency matters more than perfect formatting.

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STEP 4: Start Small Research Ideas

Good beginner directions:

• Norway-related AI support tools
• immigrant support chatbot
• Norwegian language sentiment analysis
• multilingual AI assistant
• Urdu NLP tasks
• Roman Urdu classification
• Urdu fake news detection
• Urdu summarization

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Best Free Tools

• Google Colab: free GPU for experiments
• Kaggle: datasets + notebook practice
• Hugging Face: pretrained NLP models
• GitHub: long-term portfolio
• Papers With Code: paper + implementation bridge

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Ideal Learning Order

• Month 1: Python + Pandas + ML basics
• Month 2: scikit-learn + NLP basics
• Month 3: Transformers + Hugging Face
• Month 4: multilingual AI project
• Month 5-6: read papers + reproduce experiments
• Month 7+: write research-style report/paper

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Most Important Advice

Do NOT:

• jump between 20 AI topics
• buy expensive random courses
• focus only on theory
• wait for university admission before starting

Do:

1. learn
2. build
3. publish
4. improve gradually

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Suggested Follow-On Articles

• Kaggle Python Course: Practical and Fast Track for AI Beginners
• Machine Learning Basics (3-4 Weeks): Supervised Learning and 3 Core Projects