Natural Language Processing Training
450,00 EUR
- 50 hours
The Natural Language Processing (NLP) course provides an in-depth exploration of how machine learning algorithms are used to analyze and process vast amounts of natural language data. As NLP continues to drive advancements in AI, this course equips you with the essential skills to pursue a career as an NLP Engineer. Throughout the course, you will delve into key concepts such as statistical machine translation, neural models, deep semantic similarity models (DSSM), neural knowledge base embedding, and deep reinforcement learning techniques. Additionally, you will explore the application of neural models in image captioning and visual question answering, leveraging Python’s Natural Language Toolkit (NLTK).
Key Features
Language
Course and material in English
Level
Beginner - Intermediate level
1 year access
to the platform & class recordings
6 hours of video lessons
28 hours online live class (Flexible registration)
Study Time
50 hours of study time recommendation
Virtual Lab included
and 2 course-end project
Practice
2 Assessment test
No exam
but certification of completion included
Learning Outcomes
At the end of this course, you will be able to:
Perform Text Processing
Understand and implement techniques to preprocess and analyze textual data effectively.
Develop NLP Modules
Create functional NLP components capable of tasks such as language modeling and text generation
Build Speech Models
Design basic models that can convert speech to text and vice versa, facilitating seamless human-computer interaction
Work with NLP Pipelines
Construct and manage end-to-end NLP workflows, ensuring efficient data processing and model integration
Classify and Cluster Text
Apply algorithms to categorize and group similar texts, aiding in tasks like topic modeling and sentiment analysis.
eLearning Content
Working with text corpus
Lesson 1
- The course overview
- Access and use the built-in corporat of NLTK
- Loading a corpus
- Conditional frequency distribution
- Example of lexical resources
Processing Raw Text with NLTK
Lesson 2
- Working with an NLP pipeline
- Implementing Tokenization
- Regular Expressions used in Tokenization
Natural Language
Lesson 3
Practical real world example of text classification
Lesson 4
- Naive Bayes text classification
- Age Prediction Application
- Document Classifier Application
Finding useful information from piles of text
Lesson 5
- Hierarchy of ideas or chunking
- Chunking in Python NLTK
- Chinking non chunk patterns in NLTK
Text Analytics
Lesson 6
Developing a speech to text application using Python
Lesson 7
- Python speech recognition module
- Speech to text with recurrent natural networks
- Speech to text with convolutional neural networks
More topics
Lesson 8
- Feature Extraction
- Machine Learning
- Python Toolkits
- Bagging
- Deep Learning
- Demonstrations
Live Class Content
Introduction to NLP
Lesson 1
- Definition and scope of NLP
- Real-world applications and significance of NLP
- Basic terminologies such as corpus, tokenization, and syntactic analysis
Text Data Analysis
Lesson 2
- Data preprocessing techniques tokenization, stop-word removal, and stemming, lemmatization
- Text data exploration and visualization
- Feature Engineering
- Text classification - sentiment analysis using NLTK- Naive Bayes Classifier
NLP Text Vectorization
Lesson 3
- Vector representation of text - one hot encoding
- Understanding BoW technique
- TFIDF
Distributed Representations
Lesson 4
- Work embeddings and their importance in NLP
- Detailed explanation of Word2Vec and Glove embeddings
- Training and using pre-trained word embeddings
Machine Translation and Document Search
Lesson 5
- Machine translation systems and their applications
- Building a basic machine translation system
- Introduction to document search using TF-IDF and BM25
- Evaluation Metrics for machine translation and information retrieval
Sequence Models
Lesson 6
- Introduction to sequence modelling in NLP
- Recurrent Neural Networks (RNNs) and their applications
- Application of sequence models in sentiment analysis
- Challenges in training RNNs such as vanishing gradients
Attention Models
Lesson 7
- Sequence to sequence models
- Introduction to attention mechanisms in NLP
- In-depth exploration of the transformer architecture
- Modern NLP Models like BERT and GPT which utilize attention mechanisms
Audio Analytics
Lesson 8
- Python exosystem for audio analytics
- Reading and playing audio files using Python libraries
- Load, visualize, and manipulate audio data
Digital Signal Processing and Feature Extraction
Lesson 9
- Basics of signal processing
- Frequency domain analysis using python
- Introduction to MFCCs and other spectral features
- Implementation of feature extraction in Python
- Compare different feature extraction techniques
Deep Learning for Speech
Lesson 10
- Application of machine learning in audio
- Building deep learning models for speech recognition
- Transfer learning for speech recognition
Audio Synthesis and Generative Models for Audio
Lesson 11
- Introduction to generative adversarial networks (GANs) for audio
- Generating realistic audio samples using GANs
- Music generation with Deep Learning
- Applying deep learning to generate music
- Understanding and implementing models for music composition
Who Should Enroll in this Program?
Natural Language Processing course is ideal for anyone who wants to become familiar with this emerging and exciting domain of artificial intelligence (AI)
Pre-requisites
Learners should have a basic understanding of math, statistics, data science, and machine learning.
Data Scientists and Analysts
Machine Learning and AI Engineers
Software Developers
Research Scholars and Academics
Business and Marketing Professionals
Students in UG/ PG programs
Frequently Asked Questions
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