Language Processing Intermediate Training
Use word embeddings and transformer models, fine-tune them on your own data and serve predictions through an API. Learn Language Processing through a clear Foundation to Advanced path, with hands-on labs, live cohort options, certification support and full LMS access.
Last updated
Self-paced video subscription
Billed yearly at ₹9,996. This includes all four levels, complete LMS access for one year, plus 20+ additional courses and 50+ tools access.
- All four Language Processing levels in one price.
- 20+ additional courses and 50+ tools included, even when subscribing to one course.
- Best fit for learners who want full-library access and flexible progress.
Live & Interactive session
Instructor-led cohort, max 8 to 10 hours, with demos, labs, Q&A, assignments and practical review.
- Live small-batch cohort from soon.
- Interactive discussion and lab troubleshooting.
- Recommended for teams that need guided implementation.
1:1 Live training
The same agenda, taught one-to-one by the trainer. Start immediately, without waiting for the next batch. Max 8 to 10 hours, one-to-one.
About Language Processing Intermediate Training.
Understand where Language Processing fits in modern software delivery, what this level covers, and how the program builds practical confidence step by step.
Program overview
Language Processing is taught as a practical technical skill for modern engineering, operations and platform teams. The course explains where the tool fits, how it is used in real delivery environments, and how learners can apply it through guided practice.
This DevOpsSchool Language Processing program is built for working professionals who want usable skills instead of only conceptual exposure. The training emphasizes live examples, workflow clarity, operational controls and progressive skill depth from Foundation to Intermediate.
Private Language Processing training for delivery teams, platform groups and engineering organizations.
DevOpsSchool provides corporate Language Processing training for teams that need faster adoption, workflow standardization, administrative control, integration guidance and governance-ready rollout. Private batches can be aligned to development practice, release workflow, access model, maturity level and internal tooling.
- Private online or classroom delivery for engineering teams
- Aligned to Language Processing usage maturity, admin depth and collaboration patterns
- Useful for rollout, migration, governance and workflow consistency goals
- Can be tailored for developers, administrators, DevOps teams and leadership stakeholders
Why teams use DevOpsSchool
DevOpsSchool combines live demonstration, practical workflow discussion, implementation-oriented examples, and strong post-session learning support. That makes the training more usable for real teams than generic slide-led courses.
Choose any level. The page still shows the full progression.
Foundation, Essential, Intermediate and Advanced are presented as one linked training flow. Self-paced buyers get all levels by default, even if they start from only one course page.
Instructor-led, self-paced and corporate delivery options.
Compare delivery format, duration, level access and batch style. Fees are shown only in the top decision panel and once again after the agenda.
| Duration | Mode | Level access | Batch |
|---|---|---|---|
| 8 to 10 Hrs. max | Live & Interactive | Intermediate | Public batch |
| 8 to 10 Hrs. max | 1:1 Live training | Intermediate | Starts immediately |
| 1 year LMS access | Self-paced video | All 4 levels | Anytime access |
| 3 Days | Corporate | Language Processing Intermediate Training | Private team batch |
What learners should be able to do after Language Processing training.
The outcomes are written for practical delivery teams: developers, DevOps engineers, release engineers, administrators and technical leads.
Process and represent text
Clean, tokenize and vectorise real text data with Python NLP libraries.
Build and evaluate models
Train, fine-tune and measure classic and transformer NLP models for classification, extraction and search.
Deliver production NLP
Deploy, evaluate and monitor LLM and retrieval-augmented applications responsibly.
Why this skill, what you will achieve, and who should attend.
Use this section to decide whether the course matches your role, current experience and expected Language Processing responsibilities before reviewing the detailed agenda.
Why Language Processing matters
- Language Processing helps teams standardize a critical part of their software delivery, infrastructure, automation or operations workflow.
- It gives learners a repeatable way to move from basic usage to confident execution in team and enterprise environments.
- For DevOps and platform teams, Language Processing becomes more valuable when connected with automation, governance, monitoring and delivery practices.
- Teams using Language Processing effectively reduce ad-hoc execution and move toward consistent engineering standards.
Objectives
- Understand Language Processing concepts, architecture, terminology and operational usage patterns.
- Learn practical configuration, workflow design, access controls, team usage and quality practices.
- Apply Language Processing in real delivery workflows using automation, integrations, traceability and governance patterns.
- Build practical confidence to use Language Processing for individual, team and organizational use cases.
Pre-requisites
- Basic computer literacy and familiarity with software development workflows.
- Helpful but not mandatory: introductory DevOps, cloud, automation or software delivery awareness.
- No deep programming background is required for the Foundation and Essential levels.
Who this course is for
- Developers, administrators and engineers who need practical Language Processing usage skills.
- DevOps engineers, release engineers, SREs and platform teams managing delivery standards.
- Engineering leads and technical managers responsible for workflow quality, access controls and governance.
- Corporate teams migrating to or standardizing on Language Processing.
Small-batch live training starts every month.
Weekday batches start on the 1st and weekend batches on the first Saturday of every month. One-to-one (1:1) training can start immediately; corporate sessions are scheduled on demand.
Weekend live batch
Starts on the first Saturday of every month. Weekend sessions with demos, labs and Q&A; max 10 learners.
Weekday live batch
Starts on the 1st of every month. Weekday sessions for working professionals and distributed teams; max 10 learners.
Ask advisorAgenda depth up to the selected level.
This page shows the learning flow from Foundation through Intermediate. Each level adds to the previous one, and the agenda stops at the current page level. Day counts are for corporate (day-wise) delivery; live online batches cover the same agenda in max 8 to 10 hours.
Foundation Level: NLP concepts, Python setup and text preprocessingDay 1
- What natural language processing is: tasks such as classification, extraction, search, translation and generation
- NLP pipeline overview: data collection, preprocessing, representation, modelling, evaluation and deployment
- Environment setup: Python, virtual environments, Jupyter, NLTK, spaCy, scikit-learn and Hugging Face libraries
- Working with text in Python: Unicode, encodings, string methods and reading files, CSV and JSON
- Regular expressions for text: patterns, groups, re.findall and re.sub for cleaning and extraction
- Tokenization: word, sentence and subword tokens with NLTK, spaCy and Hugging Face tokenizers
- Normalization: lowercasing, punctuation, stop words, stemming (Porter, Snowball) and lemmatization
- Linguistic basics: morphology, syntax, part-of-speech tags and dependency structure
- Corpora and datasets: NLTK corpora, Hugging Face Datasets and building your own labelled corpus
- Counting and exploring text: frequency distributions, n-grams, collocations and word clouds
- First working example: a cleaned word-frequency report from a set of documents
Essential Level = Foundation + text representation, classic models and spaCyDay 2 · 2 days cumulative
- Bag-of-words and TF-IDF: CountVectorizer, TfidfVectorizer, vocabulary size and sparse matrices
- Text classification: Naive Bayes, logistic regression and linear SVM pipelines in scikit-learn
- Evaluation: train and test split, cross-validation, precision, recall, F1 and confusion matrices
- Sentiment analysis: lexicon approaches (VADER) versus supervised models
- spaCy pipelines: nlp objects, Doc, Token and Span, pipeline components and model packages
- Syntax analysis: part-of-speech tagging and dependency parsing with spaCy and displaCy visualisation
- Named entity recognition: pretrained NER, entity labels and rule-based EntityRuler patterns
- Rule-based matching: spaCy Matcher and PhraseMatcher for domain terms and patterns
- Text similarity: cosine similarity, document clustering with k-means and topic modelling with LDA
- Handling imbalanced and noisy text data: resampling, class weights and error analysis
- Saving and reusing models: joblib pipelines and spaCy model packaging
Intermediate Level = Essential + embeddings, transformers and fine-tuningDay 3 · 3 days cumulative
- Word embeddings: Word2Vec, GloVe and fastText, training with Gensim and exploring analogies
- Sequence models: RNN, LSTM and GRU for text, and why attention replaced them
- Transformer architecture: self-attention, encoder, decoder and encoder-decoder models
- Hugging Face Transformers: pipelines, AutoTokenizer, AutoModel and the model hub
- Fine-tuning for classification: BERT-family models with the Trainer API and the evaluate library
- Token classification: fine-tuning for NER and handling subword label alignment
- Sentence embeddings and semantic search: sentence-transformers, vector similarity and FAISS indexes
- Summarisation and translation: sequence-to-sequence models such as T5 and BART
- Question answering: extractive QA with SQuAD-style models
- Multilingual NLP: multilingual models, language detection and tokenisation issues
- Serving NLP models: FastAPI endpoints, batching, model loading and Docker packaging
- Troubleshooting: tokenizer mismatches, sequence length limits, overfitting and GPU memory errors
Choose live training or a full year of self-paced access.
If someone buys Foundation, Essential, Intermediate or Advanced as self-paced video, the price stays the same and access is granted to all four levels by default.
Self-paced video subscription
Billed yearly at ₹9,996. This includes all four levels, complete LMS access for one year, plus 20+ additional courses and 50+ tools access.
- All four Language Processing levels in one price.
- 20+ additional courses and 50+ tools included, even when subscribing to one course.
- Best fit for learners who want full-library access and flexible progress.
Live & Interactive session
Instructor-led cohort, max 8 to 10 hours, with demos, labs, Q&A, assignments and practical review.
- Live small-batch cohort from soon.
- Interactive discussion and lab troubleshooting.
- Recommended for teams that need guided implementation.
1:1 Live training
The same agenda, taught one-to-one by the trainer. Start immediately, without waiting for the next batch. Max 8 to 10 hours, one-to-one.
- Start immediately: no waiting for the monthly batch.
- One learner, one trainer: full attention on your questions.
- Sessions planned around your working hours.
- Your pace, with examples from your own projects and tools.
Method of training and assessment.
The course emphasizes practical demonstration, hands-on labs and assignment-based validation instead of passive lecture-only learning.
| SL | Method of training and assessment | Weightage |
|---|---|---|
| 1 | Understanding the problem space | 5% |
| 2 | Concept discussion | 10% |
| 3 | Live demo and walkthrough | 25% |
| 4 | Lab and hands-on exercises | 50% |
| 5 | Assignments and evaluation | 10% |
Labs, exercises and real workflow practice.
Every level moves from explanation to hands-on practice, review and troubleshooting.
Build a text preprocessing pipeline
Write a Python pipeline that loads documents, cleans them with regex, tokenizes, removes stop words and lemmatizes, then reports top terms and bigrams.
Classify support tickets and extract entities
Train a TF-IDF and logistic regression classifier for support tickets, evaluate it with F1 and a confusion matrix, and add spaCy NER to pull product and customer names.
Fine-tune and serve a transformer classifier
Fine-tune a DistilBERT model on a labelled dataset, compare it with the TF-IDF baseline and serve it behind a FastAPI endpoint in a Docker container.
Language Processing is taught as part of a working delivery ecosystem.
You learn where the tool sits in the wider engineering workflow, not in isolation.
Core foundations
Terminology, concepts, architecture and the operating model behind the tool.
Workflow traceability
Map practical work to tasks, approvals, documentation and delivery visibility.
Automation and integration
Use Language Processing with validation checks, automation hooks and delivery workflows.
Governance controls
Operate with permissions, approvals, policy checks and production-safe workflows.
How your practical work is assessed.
Your certificate is backed by labs, assignments, capstone thinking and completion evidence.
Assignments
Level-specific assignments verify setup, configuration, workflow quality and operating discipline.
Hands-on validation
Learners are expected to complete practical usage, automation and troubleshooting exercises.
Completion record
Certificate issuance is tied to demonstrated participation, practical completion and course-level coverage.
Led by Rajesh Kumar and the DevOpsSchool practitioner network.
Training is grounded in practical DevOps, SRE, security, release and platform engineering experience with real enterprise delivery context.
Earn a DevOpsSchool Language Processing certificate.
Completion includes a digital certificate with credential details, practical assignments and a learning record aligned to the selected Language Processing level.
Certificate verificationCertificate of Completion
Language Processing Intermediate Training
Unique credential ID, verifiable online
What learners say about training with Rajesh Kumar.
“Rajesh is very good trainer. Rajesh was able to resolve our queries and question effectively. We really liked the hands-on examples covered during this training program.”
“Very well organized training, helped a lot to understand the AWS concept and detailed related to various tools. Very helpful”
“The training was very useful and interactive. Rajesh helped develop the confidence of all.”
Rated 4.6 on Google · All testimonials · Video reviews on YouTube
Quoted as published, from learners in Rajesh Kumar's live trainings across DevOpsSchool courses.
Used by developers, DevOps engineers, release leads and engineering managers.
Private batches are available for teams that want the same agenda aligned to their own tools and workflows.
Why this Language Processing path is different.
The value is not only one course. The subscription unlocks the full learning ecosystem for one year.
| Feature | DevOpsSchool Language Processing Training | Typical short course |
|---|---|---|
| Levels | Foundation, Essential, Intermediate, Advanced | Single generic syllabus |
| Self-paced access | All four levels plus 20+ courses and 50+ tools | Usually one course only |
| Live training | Demos, labs, Q&A and assignment review | Often lecture-heavy |
| Certificate | DevOpsSchool completion certificate | Varies by provider |
Questions learners ask before joining.
Does Foundation cost less than Advanced?
No. Self-paced pricing is intentionally the same because access includes all four levels by default.
What is included in the yearly subscription?
One year of complete LMS access, all four levels for the selected course, plus 20+ additional courses and 50+ tools access.
Can corporate teams request customization?
Yes. The live format can be aligned to team tools, governance requirements, workflows and internal delivery standards.
What is the recommended sequence?
Foundation → Essential → Intermediate → Advanced. The agenda is designed so each level builds on the previous one.
Talk to DevOpsSchool before choosing the batch.
Use this support section for batch questions, corporate planning, LMS access clarifications, or guidance on which Language Processing level fits your team.
India / Worldwide: +91 99057 40781
USA: +1 (469) 756-6329
Email: contact@DevOpsSchool.com
Chat on WhatsAppExtend the course with articles, explainers and workflow guidance.
Continue learning with supporting articles that connect Language Processing practices to DevOps, automation, governance and delivery workflows.
Training snapshots and certificate preview.
A look at the completion certificate and the course.
Certificate preview
See what course completion evidence looks like.
Tool-focused training
Course visuals reinforce the Language Processing workflow and subject area.
Live batch experience
Training supports live online, private team and workshop-style delivery.
Start Language Processing Intermediate training.
Next weekday batch starts on the 1st; weekend batch on the first Saturday. Batches are capped at 10 learners. Send your details and DevOpsSchool will share the syllabus and batch options.