Coming Soon Natural Language Processing & Language AI cover

AI & Deep Learning

Natural Language Processing & Language AI Specialization

A deep NLP specialization that starts with text foundations, moves through probabilistic and neural methods, and culminates in BERT, GPT, RAG, and modern evaluation.

📚 6 units 🪜 50 steps / lessons ⏱️ Self-paced

This course / diploma is coming soon

This course is coming soon — register your details and we'll notify you when it launches.

Expected launch: Q3 2026

Pricing on launch

Available as part of AI diploma bundles

الكورس ده قيد التحضير — سجل بياناتك وهنبلغك أول ما ينزل.

🚀 Enrollment opens soon 🔔 Sign up to be notified

Outcomes3

Tools0

Projects0

CertificateVerified

What you will learn

✅ Build modern NLP intuition from first principles to LLM systems
✅ Work with transformers, retrieval, and evaluation frameworks
✅ Prepare for language AI engineering and applied research roles

Curriculum & units

📚 6 units 🪜 50 steps ⏱️ Flexible

Unit 1: NLP Foundations 22 topics · Flexible pace

1.1.1 Intro to Text & Preprocessing — Module intro and roadmap
1.1.2 Intro to Text & Preprocessing — Why NLP is hard (Ambiguity, Sarcasm)
1.1.3 Intro to Text & Preprocessing — NLP tasks and Taxonomy
1.1.4 Intro to Text & Preprocessing — CVs and NLP analogy
1.1.5 Intro to Text & Preprocessing — Text preprocessing pipeline.
1.1.6 Intro to Text & Preprocessing — Text preparation steps
1.2.1 Linguistic Analysis — Part-of-Speech (POS) Tagging
1.2.2 Linguistic Analysis — Named Entity Recognition (NER)
1.2.3 Linguistic Analysis — Dependency Parsing & Constituency Trees
1.3.1 Sparse Vector Spaces — DL in NLP and Bag-of-Words model
1.3.2 Sparse Vector Spaces — Text features: Binary-Count- Freq- TF-IDF
1.3.3 Sparse Vector Spaces — BoW Vectors model
1.4.1 Dense Word Embeddings — Why word embeddings?
1.4.2 Dense Word Embeddings — Traditional word vectors (SVD/LSA)
1.4.3 Dense Word Embeddings — Learnable Embedding matrix
1.4.4 Dense Word Embeddings — Pre-trained word embeddings
1.4.5 Dense Word Embeddings — Word2Vec (Skip-gram/CBOW)
1.4.6 Dense Word Embeddings — GloVe
1.4.7 Dense Word Embeddings — FastText and ELMo
1.5.1 Vector Applications — Overview of recommender systems
1.5.2 Vector Applications — Content-based recommendations (Vector Similarity)
1.5.3 Vector Applications — Collaborative-Filtering (Matrix Factorization)

Unit 2: Probabilistic NLP 7 topics · Flexible pace

2.1.1 Statistical Models — Statistical Language Models (SLM)
2.1.2 Statistical Models — N-Grams & Markov Assumptions
2.1.3 Statistical Models — Spam/sentiment classification
2.2.1 Probabilistic Classifiers — Naive Bayes Idea
2.2.2 Probabilistic Classifiers — Laplace Smoothing
2.2.3 Probabilistic Classifiers — HMM use cases & Viterbi intuition
2.2.4 Probabilistic Classifiers — Conditional Independence

Unit 3: Neural NLP 16 topics · Flexible pace

3.1.1 Recurrent Networks (RNNs) — Neural Language Models (NLM)
3.1.2 Recurrent Networks (RNNs) — Recurrent Neural Networks
3.1.3 Recurrent Networks (RNNs) — RNN as Sentence Embedding Encoder
3.1.4 Recurrent Networks (RNNs) — Example RNN char/word level NLM
3.1.5 Recurrent Networks (RNNs) — Backpropagation Through Time (BPTT)
3.2.1 Gated Architectures — LSTM and Gated Recurrent Units (GRU)
3.2.2 Gated Architectures — Example: LSTM/GRU for Text Classification
3.2.3 Gated Architectures — Conv1D and CNN-LSTM models
3.3.1 Seq2Seq & Translation — Seq2seq models overview
3.3.2 Seq2Seq & Translation — Unaligned/Matched sequences case (CTC loss)
3.3.3 Seq2Seq & Translation — Statistical Machine Translation (SMT) context
3.3.4 Seq2Seq & Translation — Neural Machine Translation (NMT) & Vanilla seq2seq
3.3.5 Seq2Seq & Translation — NMT decoding and Beam-Search
3.4.1 The Attention Bridge — Attention mechanisms with seq2seq models
3.4.2 The Attention Bridge — The Information Bottleneck Problem
3.4.3 The Attention Bridge — Dot-Product Attention Math

Unit 4: Transformers 9 topics · Flexible pace

4.1.1 The Transformer Core — Attention is ALL you need
4.1.2 The Transformer Core — Self-Attention and Multi-Head Attention
4.1.3 The Transformer Core — Encoder-Decoder Architecture
4.2.1 Transfer Learning & BERT — Transfer Learning Module intro
4.2.2 Transfer Learning & BERT — Word vs Sentence Level Transfer Learning
4.2.3 Transfer Learning & BERT — BERT (Encoder) models
4.2.4 Transfer Learning & BERT — XLTransformer and XLNet
4.2.5 Transfer Learning & BERT — Distillation (DistilBERT)
4.2.6 Transfer Learning & BERT — Fine-tuning, datasets

Unit 5: GPTs / LLM Deep Dive 19 topics · Flexible pace

5.1.1 GPT Architecture — GPT (Decoder) models
5.1.2 GPT Architecture — Decoder-only, causal masking, positional encodings
5.1.3 GPT Architecture — Scaling Laws (Chinchilla) & Compute Optimality
5.1.4 GPT Architecture — Context windows (RoPE/ALiBi)
5.2.1 Tokenization & Prompting — Tokens vs words, formatting, logprobs
5.2.2 Tokenization & Prompting — Glitch Tokens & Multilingual issues
5.2.3 Tokenization & Prompting — Chain-of-Thought (CoT) Prompting
5.3.1 Pretraining & SFT — Pretraining objective, instruction datasets, safety filters
5.3.2 Pretraining & SFT — Supervised Fine-Tuning (SFT) Pipelines
5.3.3 Pretraining & SFT — Efficient Fine-Tuning (PEFT/LoRA)
5.3.4 Pretraining & SFT — FP16 vs INT4, GGUF formats, and weight compression
5.4.1 Alignment (RLHF/DPO) — RLHF overview, DPO idea, reward models
5.4.2 Alignment (RLHF/DPO) — Constitutional AI & Guardrails
5.4.3 Alignment (RLHF/DPO) — Ethics & Bias in Large Models
5.5.1 Tool use and function Calling — Structured outputs, function schemas, tool selection, error recovery
5.6.1 RAG (NLP Perspective) — Chunking, embeddings, reranking, hybrid retrieval
5.6.2 RAG (NLP Perspective) — Grounding & citations, hallucination reduction
5.6.3 RAG (NLP Perspective) — Vector DBs & GraphRAG (Using Knowledge Graphs for NLP Context)
5.6.4 Agentic NLP & Multi-Agent Systems — Moving from text generation to action. We will cover the ReAct framework (Reasoning + Acting), autonomous agent design, memory, planning, and multi-agent orchestration using modern frameworks (e.g., LangGraph, AutoGen, CrewAI).

Unit 6: NLP Evaluation 9 topics · Flexible pace

6.1 Traditional Metrics — BLEU/ROUGE idea, slice analysis
6.1 Traditional Metrics — Evaluation of Word Embedding vectors
6.1 Traditional Metrics — Language Models evaluation (Perplexity)
6.1 Traditional Metrics — Evaluation of seq2seq models (WER)
6.2 Modern Evaluation — Human eval
6.2 Modern Evaluation — LLM-as-a-Judge (GPT-4 grading)
6.2 Modern Evaluation — RAGAS (RAG Assessment)
6.2 Modern Evaluation — Benchmarks: MMLU, GSM8K, Chatbot Arena
7.1 Production & System Metrics — Cost and latency evaluation. We will break down token economics (cost per 1k tokens), Time To First Token (TTFT), Tokens Per Second (TPS), throughput, and hardware/GPU utilization considerations for deploying LLMs at scale

Project 1 topics · Flexible pace

NLP Capstone

Projects you will build

Tools & platforms

Target audience

Engineers specializing in NLP
LLM practitioners who need foundations
Advanced students building language AI careers

Career paths

What you receive after finishing

Verification-ready certificates and HR-friendly training letters.

🏆

Verified Certificate

Official Learn in Depth completion certificate with QR verification.

Verifiable on the public verification page.

🇬🇧

English Training Letter

For international companies and overseas employment.

On official Learn in Depth letterhead, signed by the instructor.

🇪🇬

Arabic Training Letter

For local employers in MENA and university coordination.

Bilingual stamped letter ready for HR submission.

🏢

Company-Stamped Certificate

Company-stamped, for academic credit. Request it by contacting +20 155 876 5064 via WhatsApp or phone.

Issued upon request after successful completion.

Course FAQ

When will these diplomas launch? إمتى الدبلومات دي هتفتح؟

The current target is Q3 2026. Register via the form and we will send launch and pricing updates first.

المستهدف الحالي هو Q3 2026. سجّل بياناتك في الفورم وسنرسل لك أول تحديثات الإطلاق والأسعار.

Can I register right now? هل أقدر أحجز دلوقتي؟

These tracks are currently marked Coming Soon. You can browse the curriculum and leave your details to be notified when registration opens.

حاليًا المسارات في حالة Coming Soon. تقدر تتصفح المحتوى وتسجل اهتمامك لنبلغك عند فتح التسجيل.

Will there be a verified certificate? هل في شهادة موثقة؟

Yes — every track is designed around a verified certificate and hands-on project review.

أيوة — كل مسار مصمم بشهادة موثقة ومراجعة عملية للمشاريع الرئيسية.

Is the content tailored for engineers in Egypt and the Arab region? هل المحتوى مناسب للمهندسين في مصر والمنطقة العربية؟

Yes. The positioning, support, and project design target engineers in the Egyptian and Gulf markets with a strong practical hiring focus.

أيوة، التسويق والدعم وبناء المشاريع معمولين خصيصًا لمهندسين السوق المصري والخليجي مع تركيز على التوظيف العملي.

How do I register? إزاي أسجل في الكورس؟

Create your account, add the course to cart, and follow the payment steps.

سجل حسابك وأضف الكورس للسلة واتبع خطوات الدفع.

Is there a student discount? هل في خصم للطلبة؟

Yes — students get an automatic discount shown at checkout.

أيوه — الطلبة ليهم خصم خاص بيظهر أوتوماتيك.

Are courses recorded or live? هل الكورسات مسجلة ولا لايف؟

All courses are recorded so you can learn at your own pace.

كل الكورسات مسجلة عشان تتعلم في أي وقت يناسبك.

Are courses free for Palestine? هل فلسطين الكورسات مجانية؟

Yes — all courses are free for people from Palestine.

أيوه — كل الكورسات مجانية لأهل فلسطين.

What payment methods are available? إيه طرق الدفع المتاحة؟

Bank transfer, Vodafone Cash, InstaPay.

تحويل بنكي، فودافون كاش، إنستاباي.