Natural Language Processing
In The Natural Language Processing (NLP) Specialization, You Will Learn How
To Design NLP Applications That Perform Question-Answering And Sentiment
Analysis, Create Tools To Translate Languages, Summarize Text, And Even Build
Chatbots. These And Other NLP Applications Will Be At The Forefront Of The
Coming Transformation To An AI-Powered Future.
Course Duration : 45 days
Overview
The Study Of Natural Language Processing Involves Computers Understanding And Interacting With Human Speech. Key Features Include Textual Tokenization, Sentiment Analysis, Machine Translation, And Speech Recognition. What Areas Can Be Used Such As Chatbots, Language Translation Or Content Generation Using Transformer Models. This Course Will Explore NLP Basics, Real World Application And Recent Developments In The Field.
Key Highlights
- One-on-One with Industry
- Expert Guidance
- 1:1 Mock Interview
Who Can Apply?
- Individuals with a bachelor’s degree and a keen interest to learn AI and Data Science
- IT professionals looking for a career transition as Data Scientists and Artificial Intelligence Engineers
- Professionals aiming to move ahead in their IT career
- Artificial Intelligence and Business Intelligence professionals
- Developers and Project Managers
- Freshers who aspire to build their career in the field of Artificial Intelligence and Data Science
Curriculum
Fundamentals Of NLP
- What Is Nlp?
- Applications Of Nlp
- Nlp Vs. Traditional Text Processing
- Key Concepts In Nlp (Tokens, Lemmas, Stemming)
- Text Preprocessing Techniques (Tokenization, Stopword Removal, Etc.)
NLP Techniques
- Part-Of-Speech Tagging
- Named Entity Recognition (Ner)
- Syntactic Parsing (Dependency And Constituency Parsing)
- Semantic Analysis (Sentiment Analysis, Word Sense Disambiguation)
- Text Classification
Statistical And Machine Learning Approaches
- Bag-Of-Words Model
- Tf-Idf (Term Frequency-Inverse Document Frequency)
- Word Embeddings (Word2vec, Glove)
- Topic Modeling (Lda, Latent Semantic Analysis)
Deep Learning for NLP
- Recurrent Neural Networks (Rnn) In Nlp
- Simple Rnns
- Long Short-Term Memory (Lstm)
- Gated Recurrent Unit (Gru)
- Convolutional Neural Networks (Cnn) For Text
- Attention Mechanisms And Transformers
- Introduction To Attention
- Sequence-To-Sequence Models
- Transformers (Bert, Gpt, Etc.)
Advanced NLP Models and Techniques
- Transfer Learning In Nlp
- Pretrained Language Models (Bert, Gpt-3)
- Fine-Tuning Pretrained Models
- Named Entity Recognition With Transformers
- Question Answering Systems
- Summarization Techniques
- Language Generation And Conversational Agents
NLP Applications
Machine Translation
- Rule-Based Vs. Statistical Vs. Neural Machine Translation
Text Classification And Sentiment Analysis
- Spam Detection
- Sentiment Analysis In Social Media
Speech Recognition And Synthesis
- Automatic Speech Recognition (Asr)
- Text-To-Speech (Tts)
Chatbots And Virtual Assistants
- Design And Implementation
- Use Cases And Challenges
Tools And Libraries For Nlp
- Natural Language Toolkit (Nltk)
- Spacy
- Textblob
- Hugging Face Transformers
- Opennlp
- Gensim
Case Studies and Practical Examples
- Sentiment Analysis On Social Media Data
- Building A Chatbot Using Rasa
- Text Classification With Bert
- Machine Translation With Transformer Models
- Named Entity Recognition With Spacy
Ethical Considerations in NLP
- Bias And Fairness In Nlp Models
- Privacy Concerns
- Responsible Use Of Nlp Technologies
Evaluation Metrics
- Text Classification: Accuracy, Precision, Recall, F1-Score
- Machine Translation: Bleu Score, Rouge Score
- Sentiment Analysis: Accuracy, F1-Score For Positive/Negative Sentiment
Multimodal and Emerging NLP Trends
- Goal Of Multi Model
- Use Cases
- Large Language Models (Llms)
Projects
- Hands On Experience On Datasets
- End To End Unique Projects
Corporate Training
We give Corporate Employees the Training They Need to Learn & Lead
Details
Flexible Timings
36 Hours Training
Certification
24/7 Support
Courses focused on building strong foundational skills for career growth
Taking Up An NLP Course Equips You With Skills To Develop Intelligent Systems That Understand And Generate Human Language. It Opens Career Opportunities In AI, Data Science, And Software Development, And Allows You To Work On Cutting-Edge Technologies Like Chatbots, Language Translation, And Sentiment Analysis, Driving Innovation And Enhancing User Experiences.
Student Success Stories
Frequently Asked Questions
How can NLP be used in creative fields?
Natural Language Processing (NLP) enhances creativity by generating poems, composing music, and aiding in scriptwriting through dialogue generation and plot analysis. It facilitates personalized storytelling and artistic expression across various mediums.
What are the future directions of NLP?
Explainable AI: Developing NLP models that can explain their reasoning and decision-making processes.
Conversational AI: Creating chatbots and virtual assistants that can have more natural and engaging conversations.
Multilingual NLP: Improving the ability of NLP models to handle multiple languages seamlessly.
What are some career opportunities in NLP?
The field of NLP is growing rapidly, creating exciting career opportunities for:
NLP Engineers: Develop and implement NLP models for various applications.
Data Scientists: Prepare and analyse data used to train NLP models.
Computational Linguists: Bridge the gap between computer science and linguistics, working on the theoretical foundations of NLP.
How does NLP work?
NLP uses a combination of techniques, including machine learning and statistical methods, to analyze language data. This data can take the form of text, speech, or even handwriting.
What are some of the challenges in NLP?
NLP faces challenges such as ambiguity in language, difficulty in understanding context, identifying sarcasm and irony, adapting to domain-specific terminology, managing multilingual data, and addressing data scarcity issues.
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