What You'll Learn
Prerequisites
- No prior programming experience needed
- Basic computer knowledge
- A laptop with internet access
- Enthusiasm to learn!
Tools & Setup
📖 Tutorial Chapters & Curriculum
Follow the structured learning path from beginner to advanced
1
Introduction to NLP: What is NLP and why it exists
Deep, practical lesson on what is nlp and why it exists with examples, pitfalls, and production notes.
2
Introduction to NLP: NLP vs NLU vs NLG
Deep, practical lesson on nlp vs nlu vs nlg with examples, pitfalls, and production notes.
3
Introduction to NLP: How machines ‘read’ language mathematically
Deep, practical lesson on how machines ‘read’ language mathematically with examples, pitfalls, and production notes.
4
Introduction to NLP: Core NLP task landscape
Deep, practical lesson on core nlp task landscape with examples, pitfalls, and production notes.
5
Introduction to NLP: Ambiguity, context, and meaning
Deep, practical lesson on ambiguity, context, and meaning with examples, pitfalls, and production notes.
6
Introduction to NLP: A practical NLP pipeline overview
Deep, practical lesson on a practical nlp pipeline overview with examples, pitfalls, and production notes.
7
Introduction to NLP: Common datasets and data ethics
Deep, practical lesson on common datasets and data ethics with examples, pitfalls, and production notes.
8
Introduction to NLP: NLP roadmap: beginner to job-ready
Deep, practical lesson on nlp roadmap: beginner to job-ready with examples, pitfalls, and production notes.
9
Text Preprocessing & Cleaning: Text normalization that actually helps
Deep, practical lesson on text normalization that actually helps with examples, pitfalls, and production notes.
10
Text Preprocessing & Cleaning: Tokenization: rules, subwords, and trade-offs
Deep, practical lesson on tokenization: rules, subwords, and trade-offs with examples, pitfalls, and production notes.
11
Text Preprocessing & Cleaning: Stopwords: when to remove and when not to
Deep, practical lesson on stopwords: when to remove and when not to with examples, pitfalls, and production notes.
12
Text Preprocessing & Cleaning: Stemming vs lemmatization in real projects
Deep, practical lesson on stemming vs lemmatization in real projects with examples, pitfalls, and production notes.
13
Text Preprocessing & Cleaning: Regex cleaning patterns for noisy data
Deep, practical lesson on regex cleaning patterns for noisy data with examples, pitfalls, and production notes.
14
Text Preprocessing & Cleaning: Handling emojis, slang, and code-mixed text
Deep, practical lesson on handling emojis, slang, and code-mixed text with examples, pitfalls, and production notes.
15
Text Preprocessing & Cleaning: De-duplication, spelling and text repair
Deep, practical lesson on de-duplication, spelling and text repair with examples, pitfalls, and production notes.
16
Text Preprocessing & Cleaning: Building a reusable preprocessing pipeline
Deep, practical lesson on building a reusable preprocessing pipeline with examples, pitfalls, and production notes.
17
Text Representation Techniques: Bag of Words and N-grams the right way
Deep, practical lesson on bag of words and n-grams the right way with examples, pitfalls, and production notes.
18
Text Representation Techniques: TF-IDF with normalization and pitfalls
Deep, practical lesson on tf-idf with normalization and pitfalls with examples, pitfalls, and production notes.
19
Text Representation Techniques: Word2Vec intuitions and training choices
Deep, practical lesson on word2vec intuitions and training choices with examples, pitfalls, and production notes.
20
Text Representation Techniques: GloVe vs FastText: what changes in practice
Deep, practical lesson on glove vs fasttext: what changes in practice with examples, pitfalls, and production notes.
21
Text Representation Techniques: Sentence embeddings and pooling strategies
Deep, practical lesson on sentence embeddings and pooling strategies with examples, pitfalls, and production notes.
22
Text Representation Techniques: Contextual embeddings (BERT-style) basics
Deep, practical lesson on contextual embeddings (bert-style) basics with examples, pitfalls, and production notes.
23
Text Representation Techniques: Vector similarity, ANN search and HNSW
Deep, practical lesson on vector similarity, ann search and hnsw with examples, pitfalls, and production notes.
24
Text Representation Techniques: Choosing representations for your task
Deep, practical lesson on choosing representations for your task with examples, pitfalls, and production notes.
25
Classical NLP & Machine Learning: Naive Bayes for text: when it shines
Deep, practical lesson on naive bayes for text: when it shines with examples, pitfalls, and production notes.
26
Classical NLP & Machine Learning: Logistic Regression baselines that win
Deep, practical lesson on logistic regression baselines that win with examples, pitfalls, and production notes.
27
Classical NLP & Machine Learning: Linear SVM for NLP and calibration
Deep, practical lesson on linear svm for nlp and calibration with examples, pitfalls, and production notes.
28
Classical NLP & Machine Learning: Feature engineering: n-grams, char-grams, metadata
Deep, practical lesson on feature engineering: n-grams, char-grams, metadata with examples, pitfalls, and production notes.
29
Classical NLP & Machine Learning: Handling imbalance and threshold tuning
Deep, practical lesson on handling imbalance and threshold tuning with examples, pitfalls, and production notes.
30
Classical NLP & Machine Learning: Interpretability: coefficients and error slices
Deep, practical lesson on interpretability: coefficients and error slices with examples, pitfalls, and production notes.
31
Classical NLP & Machine Learning: Topic modeling with LDA: useful limits
Deep, practical lesson on topic modeling with lda: useful limits with examples, pitfalls, and production notes.
32
Classical NLP & Machine Learning: Classic pipeline in production: a reference design
Deep, practical lesson on classic pipeline in production: a reference design with examples, pitfalls, and production notes.
33
Sequence Models & Deep Learning: Why sequences matter: language as time-series
Deep, practical lesson on why sequences matter: language as time-series with examples, pitfalls, and production notes.
34
Sequence Models & Deep Learning: RNN basics and the vanishing gradient
Deep, practical lesson on rnn basics and the vanishing gradient with examples, pitfalls, and production notes.
35
Sequence Models & Deep Learning: LSTM: gates, memory, and intuition
Deep, practical lesson on lstm: gates, memory, and intuition with examples, pitfalls, and production notes.
36
Sequence Models & Deep Learning: GRU: a simpler alternative
Deep, practical lesson on gru: a simpler alternative with examples, pitfalls, and production notes.
37
Sequence Models & Deep Learning: Embeddings + RNN for classification
Deep, practical lesson on embeddings + rnn for classification with examples, pitfalls, and production notes.
38
Sequence Models & Deep Learning: Seq2Seq with attention (pre-transformer)
Deep, practical lesson on seq2seq with attention (pre-transformer) with examples, pitfalls, and production notes.
39
Sequence Models & Deep Learning: Training tricks: padding, masking, teacher forcing
Deep, practical lesson on training tricks: padding, masking, teacher forcing with examples, pitfalls, and production notes.
40
Sequence Models & Deep Learning: When to choose sequence models today
Deep, practical lesson on when to choose sequence models today with examples, pitfalls, and production notes.
41
Attention Mechanism & Transformers: Attention intuition: alignments and focus
Deep, practical lesson on attention intuition: alignments and focus with examples, pitfalls, and production notes.
42
Attention Mechanism & Transformers: Self-attention step-by-step
Deep, practical lesson on self-attention step-by-step with examples, pitfalls, and production notes.
43
Attention Mechanism & Transformers: Multi-head attention and what heads learn
Deep, practical lesson on multi-head attention and what heads learn with examples, pitfalls, and production notes.
44
Attention Mechanism & Transformers: Positional encoding and sequence order
Deep, practical lesson on positional encoding and sequence order with examples, pitfalls, and production notes.
45
Attention Mechanism & Transformers: Transformer encoder vs decoder
Deep, practical lesson on transformer encoder vs decoder with examples, pitfalls, and production notes.
46
Attention Mechanism & Transformers: Training transformers: masks and efficiency
Deep, practical lesson on training transformers: masks and efficiency with examples, pitfalls, and production notes.
47
Attention Mechanism & Transformers: Common transformer variants (BERT, GPT, T5)
Deep, practical lesson on common transformer variants (bert, gpt, t5) with examples, pitfalls, and production notes.
48
Attention Mechanism & Transformers: Practical transformer pitfalls and debugging
Deep, practical lesson on practical transformer pitfalls and debugging with examples, pitfalls, and production notes.
49
Modern NLP & Large Language Models (LLMs): Pretraining objectives: MLM, CLM, denoising
Deep, practical lesson on pretraining objectives: mlm, clm, denoising with examples, pitfalls, and production notes.
50
Modern NLP & Large Language Models (LLMs): Fine-tuning strategies that work
Deep, practical lesson on fine-tuning strategies that work with examples, pitfalls, and production notes.
51
Modern NLP & Large Language Models (LLMs): Prompting and instruction tuning basics
Deep, practical lesson on prompting and instruction tuning basics with examples, pitfalls, and production notes.
52
Modern NLP & Large Language Models (LLMs): Embeddings APIs and semantic search
Deep, practical lesson on embeddings apis and semantic search with examples, pitfalls, and production notes.
53
Modern NLP & Large Language Models (LLMs): RAG overview: retrieve then generate
Deep, practical lesson on rag overview: retrieve then generate with examples, pitfalls, and production notes.
54
Modern NLP & Large Language Models (LLMs): Hallucinations: causes and mitigations
Deep, practical lesson on hallucinations: causes and mitigations with examples, pitfalls, and production notes.
55
Modern NLP & Large Language Models (LLMs): Evaluation of LLM apps: offline + online
Deep, practical lesson on evaluation of llm apps: offline + online with examples, pitfalls, and production notes.
56
Modern NLP & Large Language Models (LLMs): Cost, latency, and scaling LLM systems
Deep, practical lesson on cost, latency, and scaling llm systems with examples, pitfalls, and production notes.
57
Named Entity Recognition & POS Tagging: POS tagging: rule-based to neural
Deep, practical lesson on pos tagging: rule-based to neural with examples, pitfalls, and production notes.
58
Named Entity Recognition & POS Tagging: NER with BIO tagging and spans
Deep, practical lesson on ner with bio tagging and spans with examples, pitfalls, and production notes.
59
Named Entity Recognition & POS Tagging: spaCy NER pipeline deep dive
Deep, practical lesson on spacy ner pipeline deep dive with examples, pitfalls, and production notes.
60
Named Entity Recognition & POS Tagging: Transformers for NER (tokenization issues)
Deep, practical lesson on transformers for ner (tokenization issues) with examples, pitfalls, and production notes.
61
Named Entity Recognition & POS Tagging: Entity linking and normalization
Deep, practical lesson on entity linking and normalization with examples, pitfalls, and production notes.
62
Named Entity Recognition & POS Tagging: Evaluation for tagging: span-level metrics
Deep, practical lesson on evaluation for tagging: span-level metrics with examples, pitfalls, and production notes.
63
Named Entity Recognition & POS Tagging: Domain adaptation for NER
Deep, practical lesson on domain adaptation for ner with examples, pitfalls, and production notes.
64
Named Entity Recognition & POS Tagging: Production NER: patterns, rules, and hybrids
Deep, practical lesson on production ner: patterns, rules, and hybrids with examples, pitfalls, and production notes.
65
Sentiment Analysis & Text Classification: Sentiment analysis: problem framing
Deep, practical lesson on sentiment analysis: problem framing with examples, pitfalls, and production notes.
66
Sentiment Analysis & Text Classification: Rule-based sentiment vs ML
Deep, practical lesson on rule-based sentiment vs ml with examples, pitfalls, and production notes.
67
Sentiment Analysis & Text Classification: Deep learning sentiment with transformers
Deep, practical lesson on deep learning sentiment with transformers with examples, pitfalls, and production notes.
68
Sentiment Analysis & Text Classification: Multi-label and aspect-based sentiment
Deep, practical lesson on multi-label and aspect-based sentiment with examples, pitfalls, and production notes.
69
Sentiment Analysis & Text Classification: Toxicity and safety classification
Deep, practical lesson on toxicity and safety classification with examples, pitfalls, and production notes.
70
Sentiment Analysis & Text Classification: Error analysis and bias checks
Deep, practical lesson on error analysis and bias checks with examples, pitfalls, and production notes.
71
Sentiment Analysis & Text Classification: Serving classifiers with confidence scores
Deep, practical lesson on serving classifiers with confidence scores with examples, pitfalls, and production notes.
72
Sentiment Analysis & Text Classification: Monitoring drift in text classifiers
Deep, practical lesson on monitoring drift in text classifiers with examples, pitfalls, and production notes.
73
Machine Translation & Text Generation: Machine translation fundamentals
Deep, practical lesson on machine translation fundamentals with examples, pitfalls, and production notes.
74
Machine Translation & Text Generation: Seq2Seq translation with attention
Deep, practical lesson on seq2seq translation with attention with examples, pitfalls, and production notes.
75
Machine Translation & Text Generation: Transformer translation and beam search
Deep, practical lesson on transformer translation and beam search with examples, pitfalls, and production notes.
76
Machine Translation & Text Generation: Text summarization: extractive vs abstractive
Deep, practical lesson on text summarization: extractive vs abstractive with examples, pitfalls, and production notes.
77
Machine Translation & Text Generation: Decoding strategies: greedy, top-k, nucleus
Deep, practical lesson on decoding strategies: greedy, top-k, nucleus with examples, pitfalls, and production notes.
78
Machine Translation & Text Generation: Controlling generation: style, length, safety
Deep, practical lesson on controlling generation: style, length, safety with examples, pitfalls, and production notes.
79
Machine Translation & Text Generation: Evaluation: BLEU, ROUGE, human eval
Deep, practical lesson on evaluation: bleu, rouge, human eval with examples, pitfalls, and production notes.
80
Machine Translation & Text Generation: Production generation: guardrails and caching
Deep, practical lesson on production generation: guardrails and caching with examples, pitfalls, and production notes.
81
Question Answering & Chatbots: Chatbots: intents, slots, and flows
Deep, practical lesson on chatbots: intents, slots, and flows with examples, pitfalls, and production notes.
82
Question Answering & Chatbots: Retrieval-based QA with BM25 + vectors
Deep, practical lesson on retrieval-based qa with bm25 + vectors with examples, pitfalls, and production notes.
83
Question Answering & Chatbots: Reading comprehension QA (extractive)
Deep, practical lesson on reading comprehension qa (extractive) with examples, pitfalls, and production notes.
84
Question Answering & Chatbots: Generative QA with RAG
Deep, practical lesson on generative qa with rag with examples, pitfalls, and production notes.
85
Question Answering & Chatbots: Conversation memory and context windows
Deep, practical lesson on conversation memory and context windows with examples, pitfalls, and production notes.
86
Question Answering & Chatbots: Tool calling and agent patterns
Deep, practical lesson on tool calling and agent patterns with examples, pitfalls, and production notes.
87
Question Answering & Chatbots: Evaluation: helpfulness, faithfulness, safety
Deep, practical lesson on evaluation: helpfulness, faithfulness, safety with examples, pitfalls, and production notes.
88
Question Answering & Chatbots: Production chat: latency, moderation, privacy
Deep, practical lesson on production chat: latency, moderation, privacy with examples, pitfalls, and production notes.
89
NLP with Python & Libraries: NLTK essentials: tokenization and corpora
Deep, practical lesson on nltk essentials: tokenization and corpora with examples, pitfalls, and production notes.
90
NLP with Python & Libraries: spaCy essentials: pipelines and components
Deep, practical lesson on spacy essentials: pipelines and components with examples, pitfalls, and production notes.
91
NLP with Python & Libraries: Gensim: topic modeling and embeddings
Deep, practical lesson on gensim: topic modeling and embeddings with examples, pitfalls, and production notes.
92
NLP with Python & Libraries: Hugging Face Transformers workflow
Deep, practical lesson on hugging face transformers workflow with examples, pitfalls, and production notes.
93
NLP with Python & Libraries: Datasets + tokenizers + trainers
Deep, practical lesson on datasets + tokenizers + trainers with examples, pitfalls, and production notes.
94
NLP with Python & Libraries: Building a mini NLP project end-to-end
Deep, practical lesson on building a mini nlp project end-to-end with examples, pitfalls, and production notes.
95
NLP with Python & Libraries: Speeding up NLP with batching and GPU
Deep, practical lesson on speeding up nlp with batching and gpu with examples, pitfalls, and production notes.
96
NLP with Python & Libraries: Best practices for reproducible NLP
Deep, practical lesson on best practices for reproducible nlp with examples, pitfalls, and production notes.
97
Evaluation Metrics in NLP: Classification metrics beyond accuracy
Deep, practical lesson on classification metrics beyond accuracy with examples, pitfalls, and production notes.
98
Evaluation Metrics in NLP: Confusion matrix and threshold tuning
Deep, practical lesson on confusion matrix and threshold tuning with examples, pitfalls, and production notes.
99
Evaluation Metrics in NLP: BLEU for translation: interpretation
Deep, practical lesson on bleu for translation: interpretation with examples, pitfalls, and production notes.
100
Evaluation Metrics in NLP: ROUGE for summarization: what it misses
Deep, practical lesson on rouge for summarization: what it misses with examples, pitfalls, and production notes.
101
Evaluation Metrics in NLP: Perplexity and language modeling
Deep, practical lesson on perplexity and language modeling with examples, pitfalls, and production notes.
102
Evaluation Metrics in NLP: Embedding evaluation and retrieval metrics
Deep, practical lesson on embedding evaluation and retrieval metrics with examples, pitfalls, and production notes.
103
Evaluation Metrics in NLP: Human evaluation and rubrics
Deep, practical lesson on human evaluation and rubrics with examples, pitfalls, and production notes.
104
Evaluation Metrics in NLP: Online evaluation: A/B, canary, feedback loops
Deep, practical lesson on online evaluation: a/b, canary, feedback loops with examples, pitfalls, and production notes.
105
NLP Deployment & Production: Packaging NLP models for APIs
Deep, practical lesson on packaging nlp models for apis with examples, pitfalls, and production notes.
106
NLP Deployment & Production: FastAPI deployment blueprint
Deep, practical lesson on fastapi deployment blueprint with examples, pitfalls, and production notes.
107
NLP Deployment & Production: Docker, images, and reproducible builds
Deep, practical lesson on docker, images, and reproducible builds with examples, pitfalls, and production notes.
108
NLP Deployment & Production: Scaling inference: batching, async, queues
Deep, practical lesson on scaling inference: batching, async, queues with examples, pitfalls, and production notes.
109
NLP Deployment & Production: Caching, rate limiting, and cost control
Deep, practical lesson on caching, rate limiting, and cost control with examples, pitfalls, and production notes.
110
NLP Deployment & Production: Observability: logs, traces, prompt metrics
Deep, practical lesson on observability: logs, traces, prompt metrics with examples, pitfalls, and production notes.
111
NLP Deployment & Production: Security and privacy for text data
Deep, practical lesson on security and privacy for text data with examples, pitfalls, and production notes.
112
NLP Deployment & Production: Deployment checklist and runbooks
Deep, practical lesson on deployment checklist and runbooks with examples, pitfalls, and production notes.
113
Advanced NLP & Research Topics: Multilingual NLP and cross-lingual transfer
Deep, practical lesson on multilingual nlp and cross-lingual transfer with examples, pitfalls, and production notes.
114
Advanced NLP & Research Topics: RAG systems with vector databases
Deep, practical lesson on rag systems with vector databases with examples, pitfalls, and production notes.
115
Advanced NLP & Research Topics: Alignment, safety, and responsible NLP
Deep, practical lesson on alignment, safety, and responsible nlp with examples, pitfalls, and production notes.
116
Advanced NLP & Research Topics: Hallucination detection and factuality
Deep, practical lesson on hallucination detection and factuality with examples, pitfalls, and production notes.
117
Advanced NLP & Research Topics: Long-context, memory, and compression
Deep, practical lesson on long-context, memory, and compression with examples, pitfalls, and production notes.
118
Advanced NLP & Research Topics: Knowledge graphs + NLP hybrids
Deep, practical lesson on knowledge graphs + nlp hybrids with examples, pitfalls, and production notes.
119
Advanced NLP & Research Topics: Evaluation at scale for LLM apps
Deep, practical lesson on evaluation at scale for llm apps with examples, pitfalls, and production notes.
120
Advanced NLP & Research Topics: Research reading: papers, ablations, reproducibility
Deep, practical lesson on research reading: papers, ablations, reproducibility with examples, pitfalls, and production notes.
🎯 Interview Preparation
Top Python interview questions organized by difficulty
Easy
40 Questions
Freshers / Entry Level
- Explain Text Preprocessing in NLP and discuss its importance with examples. (Q1)
- Explain Tokenization in NLP and discuss its importance with examples. (Q2)
- Explain Stemming vs Lemmatization in NLP and discuss its importance with examples. (Q3)
- Explain TF-IDF in NLP and discuss its importance with examples. (Q4)
- Explain Word2Vec in NLP and discuss its importance with examples. (Q5)
Medium
40 Questions
Experienced / Mid-Level
- Explain Text Preprocessing in NLP and discuss its importance with examples. (Q41)
- Explain Tokenization in NLP and discuss its importance with examples. (Q42)
- Explain Stemming vs Lemmatization in NLP and discuss its importance with examples. (Q43)
- Explain TF-IDF in NLP and discuss its importance with examples. (Q44)
- Explain Word2Vec in NLP and discuss its importance with examples. (Q45)
Hard
40 Questions
Senior / Lead Level
- Explain Text Preprocessing in NLP and discuss its importance with examples. (Q81)
- Explain Tokenization in NLP and discuss its importance with examples. (Q82)
- Explain Stemming vs Lemmatization in NLP and discuss its importance with examples. (Q83)
- Explain TF-IDF in NLP and discuss its importance with examples. (Q84)
- Explain Word2Vec in NLP and discuss its importance with examples. (Q85)
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