Natural Language Processing Training

Natural Language Processing Training

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

• Course and material in English
• Beginner - Intermediate level
• 1 year access to the platform & class recordings

• 6 hours of video lessons
• 28 hours online live class (Flexible registration)
• 50 hours of study time recommendation
• 2 course-end project
• Virtual Lab included to practice
• 2 Assessment test
• No exam but certification of completion included

Learning Outcomes:

• 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.

Target Audience

• Data Scientists and Analysts: Professionals seeking to enhance their ability to process and analyze large volumes of unstructured text data.
• Machine Learning and AI Engineers: Individuals looking to specialize in NLP to develop intelligent applications that understand and interpret human language.
• Software Developers: Programmers interested in integrating language processing capabilities into applications, such as chatbots and virtual assistants.
• Research Scholars and Academics: Those pursuing research in computational linguistics or related fields.
• Business and Marketing Professionals: Individuals aiming to leverage NLP for sentiment analysis, customer insights, and data-driven decision-making.

Eligibility

Natural Language Processing course is ideal for anyone who wants to become familiar with this emerging and exciting domain of artificial intelligence (AI), including data scientists, analytics managers, data analysts, data engineers, and data architects.

Pre-requisites

Learners are looking to enroll for Natural Language Processing course should have a basic understanding of math, statistics, data science, and machine learning.

Course ContenteLearning content

1. Working with text corpus

• 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

• Working with an NLP pipeline
• Implementing Tokenization
• Regular Expressions used in Tokenization

3. Natural Language

4. Practical real world example of text classification

• Naive Bayes text classification
• Age Prediction Application
• Document Classifier Application

5. Finding useful information from piles of text

• Hierarchy of ideas or chunking
• Chunking in Python NLTK
• Chinking non chunk patterns in NLTK

6. Text Analytics

7. Developing a speech to text application using Python

• Python speech recognition module
• Speech to text with recurrent natural networks
• Speech to text with convolutional neural networks

8. More topics

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

Live Class Content

1. Introduction to NLP

• 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

• 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

• Vector representation of text - one hot encoding
• Understanding BoW technique
• TFIDF

4. Distributed Representations

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

Will missing a live class affect my ability to complete the course?

No, missing a live class will not affect your ability to complete the course. With our 'flexi-learn' feature, you can watch the recorded session of any missed class at your convenience. This allows you to stay up-to-date with the course content and meet the necessary requirements to progress and earn your certificate. Simply visit the learning platform, select the missed class, and watch the recording to have your attendance marked.