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Advanced Natural Language Processing Course

Designed by FAANG+ AI/ML Engineers to help you transform your career

  • Dive deep into cutting-edge NLP techniques and technologies
  • Gain skills to tackle complex language processing challenges of today
  • Get hands-on experience on end-to-end NLP projects
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Instructors, Coaches & Interviewers from Top Companies

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Tools you’ll learn

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Who Is the NLP Course For?

  • Data Scientists and Machine Learning Engineers
  • AI/ML professionals already working in ML model-building roles

Prerequisites

Proficiency in Python

Basic knowledge of Probability, Statistics, Linear Algebra, and Calculus
Solid understanding of Classical Machine Learning

Curriculum Modules in the Advanced NLP Course

Overview of Deep Learning
1

Deep Learning & its applications

2

Deep Learning vs Neural Networks

3

Activation Functions

4

Loss Functions

Deep Learning Data Structures
1

Basic Tensor operations

2

NN model training using Pytorch

3

Notebooks on Keras

Feedforward Neural Network
1

FNN architecture

2

Applications & Limitations

Recurrent Neural Network
1

Backpropagation through time (BPTT)

2

Vanishing & Exploding Gradients

3

LSTM & GRU

Text Processing Basics
1

Text Cleaning & Normalization

2

Tokenization

3

Stemming/Lemmatization

4

POS tagging

5

Text Parsing & EDA

6

Text Representation (Bag of Words, TF-IDF)

Word Embeddings
1

Doc2Vec

2

Glove

Seq2Seq Modelling
1

Sentiment Analysis application

2

Deep LeSeq2Seq: Enoder-Decoder Architecturearning vs Neural Networks

Image Captioning
1

Architecture

2

Model training

Transformers
1

Transformers Architecture

2

Self Attention

2

Multi-head Attention

Transformers
1

BERT Architecture

2

RoBERTa

3

AlBERT

4

ELECTRA

5

Fine Tuning BERTs

Bonus Content: Generative AI - Diffusion Models Overview
Capstone Project: Advanced Spoiler Shield with Natural Language Processing

Leverage deep learning and use advanced NLP techniques to accurately identify spoilers in movie and TV show reviews, enhancing user experience on platforms like IMDb.

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Why choose this course?

Market-focused NLP Course

Skills, case studies, and projects that provide exposure to AI/ML applications across industries, giving you a competitive edge in the job market

Flexible Program by FAANG+ NLP Experts

25+ hours of premium video content featuring cutting-edge insights from FAANG+ engineers, offering direct access to industry best practices and emerging trends

Capstone Projects for Hands-on Experience

Complete focus on real-world applications, ensuring you gain practical, job-ready skills in Natural Language Processing

Industry-relevant Concepts and Tools

Learn to apply Neural Architectures, Transformer Architectures, and BERT models, and tools such as Colab, TensorFlow, PyTorch, and Hugging Face

Comprehensive Curriculum

End-to-end coverage of Natural Language Processing designed to cater to everyone from beginners in ML/DS to advanced practitioners

Comprehensive Coaching and Support

Weekly technical coaching sessions with industry veterans, plus 24x7 access to Teaching Assistants, ensuring prompt doubt resolution and continuous learning support

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falag + Instructors to Train You in Live Classes

Ahsan Ali

Applied Scientist

Randy Cogill

Senior Research Scientist

Shruti Goli

Senior Product Manager, Incode
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instructor

Jay Pillai

Engineering Leader - GenAI

FAANG+ Leader

The IK Experience: What Our Alumni Are Saying

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How to Enroll for Interview Kickstart’s Advanced NLP Course

Learn more about Interview Kickstart and the Advanced NLP Course by joining the free pre-enrollment webinar.

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FAQs

The best way to join the course is to first register for our pre-enrolment session here. You will learn all about the course, its cost, and other useful details.

The Advanced Natural Language Processing Program comprises self-paced classes, i.e. recorded videos. There will be live sessions for assignments and Capstone Project evaluations.

The learners can do the program at their own pace. A commitment of 8-10 hours per week is recommended. This includes time for watching the self-paced videos, completing hands-on assignments, and revisiting key concepts for practice and revision. You can complete the course, including all exercises and projects, in 6 to 8 weeks. Consistent effort will help you stay on track and absorb the material effectively, especially when working through advanced topics like BERT models, Seq2Seq architectures, and transformer-based models.

Participants should have the following prerequisites:
  • Proficiency in Python: A working knowledge of Python is essential, as all assignments and projects require coding.
  • Foundational understanding of ML Mathematics: Mathematical concepts such as Probability, Statistics, Linear Algebra, and Calculus are critical for understanding the machine learning algorithms covered in the course.
  • Strong grasp of Classical ML: Familiarity with ML concepts and models will help learners build on their knowledge and dive deeper into NLP applications.
 
 

You will not find NLP difficult to learn if you are a Data Scientist or ML Engineer. You do, however, need to have knowledge/understanding of Python, Probability, Statistics, Linear Algebra, Calculus, and Classical Machine Learning to be able to excel in this technology.

Our NLP course:
  • Is market-focused, providing exposure to AI/ML applications across industries
  • Teaches you concepts such as Neural Architectures, Transformer Architectures, and BERT models, and tools such as Colab, TensorFlow, PyTorch, and Hugging Face
  • Is designed and taught by FAANG+ NLP and AI/ML Experts who are leading the advancements and trends in this field
  • Is in-depth and comprehensive, covering all aspects of Natural Language Processing
  • Gives you practical, job-ready skills through hands-on assignments and real-world applications
  • Is completely flexible, offering you 25+ hours of premium video content + the opportunity to work on Capstone Projects with FAANG+ instructors
  • Has technical coaching sessions with industry veterans as well as 24×7 access to Teaching Assistants
Throughout the course, you’ll work on hands-on projects that reflect real-world challenges in Natural Language Processing. These projects include text processing, word embedding, and building neural networks for sentiment analysis, image captioning, and Seq2Seq modeling applications. These projects allow you to apply NLP techniques in practical scenarios, such as developing models that handle large-scale text data or building chatbots for customer service. The projects are designed to simulate industry-relevant applications, ensuring you gain practical experience that can be directly transferred to your work.
NLP is a much sought-after skill in Data Scientists and ML Engineers. Here’s how our program helps you land your dream AI/ML job:
  • The comprehensive curriculum covers the latest and best in NLP today.
  • Since it is taught by FAANG+ leaders, you get to learn from people who are at the forefront of revolution in AI tech today. 
  • Our assignments and Capstone Projects ensure that you are able to apply all the concepts you learn in practical, real-world scenarios
NLP and Generative AI overlap when machines generate text that sounds natural, like when AI writes an article, responds to emails, or completes sentences in a chatbot. NLP is the foundation that allows Generative AI to understand and produce language. Example: When ChatGPT creates a story from a prompt, it uses NLP to understand your input and generative AI to produce the story.
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