TEXT DATA / CLASSIFICATION / ENTITY EXTRACTION / EVALUATION
NLP Course
Learn to turn unstructured text into useful, measurable application outputs. This NLP Course covers text preparation, classification, entity extraction, embeddings and transformer-based approaches, with attention to data leakage, imbalance and language-specific errors.
Course outline8 modules
Practice3 project scenarios
Duration2 months
Course feeOnline ₹20,000
Classroom ₹25,000
About the NLP Course
Natural language processing is the field of building systems that analyse or produce human language. Many useful NLP tasks do not require a generative chatbot. A classifier can route a ticket, an extraction system can identify a product name and a search system can retrieve related text. You learn to choose the method from the task and evaluate it on representative examples.
This course specialises in language data and task-level modelling. The Generative AI course covers the broader creation and application landscape. The Hugging Face Course focuses on a particular ecosystem; this NLP outline compares rules, classical machine learning and neural methods without treating one library as the entire subject.
For product-specific terminology and behaviour, use spaCy language-processing concepts. Match the documentation to the environment used in your exercises.
Benefits of Learning NLP
Turn the syllabus into practical advantages you can demonstrate in your own work. These outcomes come from building, checking and explaining the exercises.
Frame a measurable language taskTranslate a broad text problem into labels, expected outputs and meaningful evaluation.
Build strong baselinesCompare simple text features with more complex models instead of assuming complexity wins.
Recognise data leakageKeep related or duplicated examples from distorting the train-test comparison.
Investigate language errorsReview ambiguity, rare terms and uneven performance across relevant text groups.
Choose suitable metricsUse task-specific measures and error examples rather than a single unexplained accuracy number.
Present a reproducible resultShow preprocessing, splits, model settings and failure analysis alongside the final output.
Who Should Join?
Basic Python, tabular data handling and introductory statistics are expected. Understanding train/test splits and supervised learning will help. You do not need to train a large language model from scratch, but you should be willing to inspect datasets and individual errors carefully.
Data science learnersBuild a strong foundation in practical language-data modelling.
Python developersAdd classification and extraction features to applications.
Analysts working with textTurn messages, feedback and documents into structured information.
For complementary learning, explore Generative AI Training. Choose the broader program if you need an introduction to AI models, prompting and the wider application landscape before specialising.
NLP Prerequisites and Readiness Checklist
Use these three checks to decide whether you are ready to start or need a short foundation step first. Bring any gaps to your demo discussion.
Python data handlingRead a text dataset, inspect missing values and work with lists, dictionaries and tabular records.
Basic statisticsUnderstand counts, averages and the meaning of precision and recall at an introductory level.
A defined text taskChoose a small permitted task, such as classification, with clear labels and a separate test set.
NLP Course Syllabus: 8 Practical Modules
Each module connects a concept to an exercise and an output you can explain. Work through the foundations before attempting the integrated project.
1. Language tasks and dataset design
Define a text problem with a clear output and evaluation criterion. Inspect the data source, labels and permitted use. Separate examples by the appropriate unit, such as customer or document, to avoid leaking near-duplicate information across splits.
2. Text preparation and tokenisation
Explore Unicode, whitespace, tokenisation and normalisation. Compare raw and cleaned text before deleting punctuation or stop words. Preserve negation, product identifiers and other task-critical signals rather than applying a cleaning recipe blindly.
3. Classical text baselines
Represent documents with counts or TF-IDF and train a simple classifier. Build preprocessing into a repeatable pipeline. Compare the baseline with a majority-class rule and inspect confusion between labels before increasing model complexity.
4. Entities, rules and structured extraction
Use rule-based matching and a suitable NLP pipeline to extract named entities or domain fields. Evaluate exact and partial matches with clear criteria. Include absent entities and ambiguous names, retaining the source span for review.
5. Embeddings and semantic similarity
Represent sentences or documents with embeddings. Compare semantic similarity with lexical matching on the same examples. Examine how a high similarity score can still be unhelpful when a question requires an exact number, date or product code.
6. Transformer-based language tasks
Use an appropriate pretrained model for classification or another agreed language task. Inspect tokenizer behaviour, truncation and context limits. Compare against the classical baseline and adapt a small model only where the data and hardware justify it.
7. Evaluation, bias and error analysis
Use precision, recall and F1 where appropriate and examine results by class and meaningful data slice. Review label quality, imbalance and language variation. Report limitations instead of presenting one aggregate score as proof of fairness or reliability.
8. NLP application capstone
Package the preprocessing and model together with tests and a clear inference interface. Deliver a dataset note, baseline comparison and error analysis. Explain how incoming text may differ from the training data and how failures should be reviewed.
Discuss Your Learning Goals
Tell the team about your experience and preferred schedule. Book a free demo to discuss the course, lab access and the practical work expected from you.
Practical Skills You Will Develop
Use the exercises to build an explanation as well as a result. You should be able to show what you did, how you checked it and where the limits are.
Task formulationTurn a vague text problem into an observable output.
Text preparationPreserve useful language signals while cleaning noise.
Baseline modellingEstablish a simple reference before adopting a larger model.
Information extractionReturn structured fields with supporting text spans.
Error analysisInspect label confusion and data slices, not just one score.
Reproducible inferenceKeep preprocessing and model versions aligned.
Tools and Lab Requirements
Use a personal practice environment with synthetic or openly licensed data. Keep credentials outside notebooks and source control. Confirm software access, hardware and any paid API usage with the course team before enrolling.
Python data toolsAn editor or notebook with libraries for data inspection and modelling.
spaCy and scikit-learnUse language pipelines, rules and classical baselines for selected tasks.
Transformer modelsA small permitted model and compatible library; additional compute is agreed separately.
Consult scikit-learn text feature extraction during practice. Use documentation matching the installed version and check applicable permissions and service costs.
Your Learning Roadmap
Build confidence in stages. Practice time and your starting knowledge matter as much as the number of scheduled sessions.
1. Set up and understandCheck the prerequisites and lab access. Complete the first two modules and explain the basic workflow in your own words.
2. Build and investigateWork through the middle modules. Keep notes of errors, what you tried and the evidence that supported a fix.
3. Test and presentComplete the final modules and an integrated scenario. Present your output, validation and a short handover guide.
Your NLP project milestone: Include the label guide, preprocessing pipeline, leakage checks, baseline comparison and class-level error analysis. Use the existing syllabus checklist to track progress; follow the stages during the 2-month course and confirm assignment dates with your trainer.
3 Practical Project Scenarios
These are teaching scenarios using approved lab systems and synthetic data. They are not claims about live client work or previous student results.
Support ticket classifierRoute synthetic or openly licensed tickets into defined categories. Compare a simple baseline with a transformer approach and inspect minority-class errors.
Product entity extractorExtract product codes and names from fictional requests. Retain the source spans and test missing, repeated and ambiguous entities.
Customer feedback analyserClassify or group permitted feedback by topic. Show how negation, mixed sentiment and unfamiliar wording affect the results.
How the Skills Work in Practice
A classifier labels almost every request as billing because most training examples belong to that category. Its overall accuracy looks strong, but it misses the smaller cancellation class. Inspect per-class recall and the confusion matrix before deciding that the model works.
Review label quality and the split strategy, then compare an appropriate adjustment with the original baseline. Keep the test set untouched. The final explanation should identify the business cost of the missed class, not merely announce a better average score.
Why Learn with Brolly Academy?
Choose a course that connects the subject to work you can actually demonstrate. Use a demo to discuss the learning path and decide whether it fits your starting point.
A connected learning pathThe eight-module outline moves from foundations to an integrated task. Each module identifies an output rather than leaving practice as a vague promise.
Discuss your own learning goalShare your experience and preferred format with the course team. Use a demo to assess whether the technical depth, pace and project expectations fit your needs.
Clear course boundariesThe page explains prerequisites, product scope and the difference between training completion and independent certification. You can compare the offer with your actual requirements.
Compare Brolly Academy with Other Training Institutes
Compare the same evidence from each provider: what you will build, what you will test and which services the fee includes. Ask for specific examples rather than relying on broad claims about advanced AI.
What to discuss with Brolly AcademyReview these eight modules and the three project scenarios.
Ask for the assigned trainer profile.
Check the lab version and access period.
Review the fee, schedule and certificate terms.
Use the demo to assess the teaching approach.
What to check with any instituteRequest a detailed outline, not just product names.
Verify the trainer information supplied.
Separate demonstrations from your own lab work.
Check licences, exam costs and refund conditions.
Compare the total commitment, not only the advertised price.
Classroom, Online and Corporate Learning
Discuss the currently available delivery format with the academy before booking. Batch availability and session timings should be agreed in writing.
Classroom learning enquiryConfirm the venue, next available batch and computer requirements before arranging travel.
Live online enquiryCheck the session time zone, remote lab access and arrangements for asking questions or catching up on a missed session.
Corporate team enquiryShare your team size, current tools and learning objectives. Agree a scoped program without sharing confidential business data.
For a team program, explore Brolly Academy corporate training.
NLP Course Fee and Duration
The NLP Course runs for 2 months. The fee is ₹20,000 for online training or ₹25,000 for classroom (offline) training. Ask for a written quotation confirming taxes, payment and refund terms, lab access and any separate software or API charges.
Classroom training: ₹25,000Learn in the classroom over 2 months. Confirm the venue, batch start date and session timings with the academy before arranging travel.
Online training: ₹20,000Choose online learning over 2 months. Confirm the session time zone, lab access and arrangements for questions or missed sessions before enrolling.
Duration: 2 monthsPlan time for lessons, independent practice and the course project. Ask for the batch calendar, instructor-led hours, assignment deadlines and support period.
Course Completion and Certification Guidance
Brolly Academy course completion recognizes the training and assessment work agreed for your batch. It is separate from a credential awarded by a software vendor or another certification body.
Brolly Academy course completionAsk for the attendance, assignment and assessment requirements, the certificate wording and how completion will be verified.
Independent certificationUse official vendor information to check whether a relevant credential is currently offered and what preparation it requires. No vendor authorization, exam voucher or pass guarantee is implied.
Career Roles and Skill Applications
Match the learning path to a role, then compare its requirements with your existing experience. The course can support skill development; it does not guarantee employment.
NLP developerBuild language-processing features with measurable outputs.
Data scientist working with textSelect and evaluate models for text datasets.
Applied ML engineerPackage and maintain language-task pipelines.
You can also explore AI Testing Training. Develop a deeper QA practice for AI answers, tool behaviour and release decisions.
Where These Skills Are Used
NLP supports ticket routing, feedback analysis, search and information extraction. The appropriate method depends on the task and data. Sensitive uses involving employment, health, credit or personal profiling require additional domain, legal and ethical review beyond a classroom model exercise.
Interview and Portfolio Preparation
Prepare evidence you can discuss clearly. Label practice work as training work and do not present a teaching scenario as paid client experience.
Explain a projectDescribe the requirement, your decisions, the result and one limitation. Be ready to answer what you would change for a real deployment.
Show useful evidenceKeep approved files, test results and a concise README or runbook. Remove secrets and private information before sharing a portfolio.
Discuss support servicesAsk the academy which resume, mock-interview or job-search services are included in your batch. Placement assistance is not a job or salary guarantee.
Placement Preparation and Career Support
Use this checklist during your demo to agree the career-support services included with your batch. The preparation below builds on your course project; recruitment and interview outcomes remain with the employer.
Choose a target roleStart with the responsibilities of a NLP developer. Compare several relevant job descriptions and note the skills you can already prove.
Prepare a project evidence packInclude the label guide, preprocessing pipeline, leakage checks, baseline comparison and class-level error analysis.
Practise a technical discussionWhy might a classifier with high overall accuracy still fail on an important minority class? Explain your decision with a test or project example.
Write an honest resume entryState the project goal, your contribution and the checks you ran. Label it as a learning project and report only results you actually measured.
Request focused feedbackAsk whether the batch includes a resume review, portfolio review or mock interview. Agree the format, number of reviews and support period before enrolling.
Plan applications and follow-upKeep a record of role requirements, applications and feedback. Use the next practice session to address a specific gap rather than adding unsupported claims to your profile.
Career Planning and Salary Expectations
Salary depends on the role, prior experience, location and the employer. A tool course alone does not establish a salary band. Compare recent job descriptions, required experience and responsibilities rather than relying on a headline income promise.
Meet Your Trainer: Dinesh
Dinesh is the trainer for the NLP Course. Use a demo to discuss your learning goals, ask about a practical NLP scenario and understand how project feedback is provided.
Relevant experienceAsk for examples of work with NLP that can be discussed without revealing confidential client information.
Practical explanationAsk the trainer to explain a failure scenario and how a learner would investigate it, rather than only showing a finished result.
Feedback and supportDiscuss how assignments are reviewed, how questions are handled and the support period included in the course.
Brolly Academy Learner Feedback
Read available academy feedback and ask whether a review relates to this particular course, trainer and delivery format. General academy reviews should not be mistaken for verified results from this new course.
The excerpts below are published on Brolly Academy's academy reviews page. They describe Data Science and Machine Learning courses, not this new NLP course.
Aditya - Data Science"The instructors provided a clear and practical understanding of data science concepts."
Read the original academy testimonial
Kavita Singh - Data Science"The hands-on projects helped me apply the concepts effectively."
Read the original academy testimonial
Rahul Desai - Machine Learning"The trainers were experienced professionals who explained complex algorithms with ease."
Read the original academy testimonial
Student Community and Continued Learning
Keep learning visible: ask precise questions, share permitted examples and record what changed after feedback. Check with the course team about any batch-specific discussion group or mentor access.
Academy updatesFollow the Brolly Academy WhatsApp channel for public academy updates. A public channel is separate from individual technical support.
NLP peer-practice ideaLabel a small permitted text sample independently and resolve disagreements before training a model.
Share safely and ask clearlyInclude the tool version, a small permitted example, the expected result and what happened. Remove API keys, employer documents and personal data before asking for help.
For continued technical learning, use spaCy language-processing concepts. Recheck examples against the version you are using.
Download Your Practice Checklists
Use these editable CSV files to organize your learning. They open in common spreadsheet tools and contain real module and project-checklist content.
Download Syllabus (CSV) Project Checklist (CSV)Official Documentation for Further Reading
Use the documentation that matches your product version and environment. These sources support technical learning; linking to them does not imply endorsement of Brolly Academy.
spaCy language-processing concepts
scikit-learn text feature extraction
Hugging Face NLP learning material
Related Courses at Brolly Academy
Choose complementary learning based on your current skill gaps. These are separate courses, not automatically included in this program.
Generative AI TrainingChoose the broader program if you need an introduction to AI models, prompting and the wider application landscape before specialising.
View Generative AI Training
Machine Learning CourseStrengthen model selection, supervised learning and evaluation foundations.
View Machine Learning Course
AI Testing TrainingDevelop a deeper QA practice for AI answers, tool behaviour and release decisions.
View AI Testing Training
To work with pretrained models, tokenizers and reusable model tooling, continue with the Hugging Face Course.
To apply text-processing and embedding knowledge to source-grounded question answering, explore RAG Training.
If your learning goal also includes visual data, the Computer Vision Course provides a separate path into image tasks and evaluation.
Talk to the NLP Course Team
Call +91 81868 44555 or send a course enquiry to discuss the syllabus and current batch options. For a classroom visit, confirm the venue and appointment with Brolly Academy before travelling.
Read our contact details for the academy location and enquiry options. Online delivery, session time zones and tool access should be confirmed for your batch.
Call +91 81868 44555NLP Course FAQs
Does NLP mean neuro-linguistic programming here?
No. On this page, NLP means Natural Language Processing, the AI and computing field concerned with text and language. It is not a personal-development or communication-coaching course.
Is NLP the same as Generative AI?
No. NLP includes classification, extraction and other language-analysis tasks as well as generation. Generative AI also includes non-language media such as images and audio.
Will I learn only chatbots?
No. The core projects cover classification, entity extraction and feedback analysis. These tasks teach measurable language-processing skills without depending on a conversational interface.
Should I always remove stop words?
No. Preprocessing depends on the task and model. Removing words can damage meaning, especially negation or short requests. You compare alternatives rather than apply a universal cleaning rule.
Do transformers always beat simple models?
No. Results depend on data, task and constraints. A simpler model can be easier to maintain and sufficient for a structured problem. The course compares both using the same evaluation setup.
Will the course cover every language?
No. Language support depends on the dataset and selected models. The lab language and any multilingual extension should be confirmed with the team before enrollment.
How much does the NLP Course cost?
The NLP Course fee is ₹20,000 for online training and ₹25,000 for classroom (offline) training. Confirm taxes, payment and refund terms, lab access and any separate software, API or examination costs in your written quotation.
What is the course duration and batch schedule?
The course duration is 2 months for both online and classroom training. Contact Brolly Academy for the next batch start date, session timings, instructor-led hours and independent practice expectations.
Can I attend a free demo before enrolling?
Use Book Free Demo to open the enquiry form, or contact the team on WhatsApp. Share your experience and learning goal so the counsellor can discuss a suitable session and current availability.
What certificate will I receive?
Discuss the Brolly Academy course-completion requirements for your batch, including attendance, assignments and project assessment. Course completion is separate from any credential issued by a software vendor or independent examination body.
Does the course guarantee a job or salary?
No. The learning path is designed to build demonstrable skills. Ask which portfolio, resume or interview-support services are included. Hiring decisions depend on your wider experience, preparation, location and employer requirements.
How should I compare this course with another institute?
Compare the actual syllabus, your own lab work, trainer experience, project feedback, tool costs and written terms. Ask each provider the same questions and use a demo to assess the teaching style before committing.
Got More Questions About NLP?
Talk to the Brolly Academy team about your starting skills, learning goal and available batch options. Request a free demo before choosing your course.