Natural Language Processing Training

450,00 EUR

  • 50 hours
Blended Learning
eLearning
Live Virtual Classroom

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

Hero

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

Hero
  1. 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
  2. Processing Raw Text with NLTK

    Lesson 2

    • Working with an NLP pipeline
    • Implementing Tokenization
    • Regular Expressions used in Tokenization
  3. Natural Language

    Lesson 3

  4. Practical real world example of text classification

    Lesson 4

    • Naive Bayes text classification
    • Age Prediction Application
    • Document Classifier Application
  5. Finding useful information from piles of text

    Lesson 5

    • Hierarchy of ideas or chunking
    • Chunking in Python NLTK
    • Chinking non chunk patterns in NLTK
  6. Text Analytics

    Lesson 6

  7. 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
  8. More topics

    Lesson 8

    • Feature Extraction
    • Machine Learning
    • Python Toolkits
    • Bagging
    • Deep Learning
    • Demonstrations

Live Class Content

Hero
  1. 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
  2. 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
  3. NLP Text Vectorization

    Lesson 3

    • Vector representation of text - one hot encoding
    • Understanding BoW technique
    • TFIDF
  4. 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
  5. 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
  6. 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
  7. 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
  8. Audio Analytics

    Lesson 8

    • Python exosystem for audio analytics
    • Reading and playing audio files using Python libraries
    • Load, visualize, and manipulate audio data
  9. 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
  10. Deep Learning for Speech

    Lesson 10

    • Application of machine learning in audio
    • Building deep learning models for speech recognition
    • Transfer learning for speech recognition
  11. 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
natural language processing

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

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Frequently Asked Questions

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