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Product description
Natural Language Processing (NLP) E-Learning
Master the power of language with deep learning and neural networks. The Natural Language Processing (NLP) Proficiency Journey unpacks the foundations, concepts, and cutting-edge advancements in Deep Learning and Neural Networks as applied to language-related tasks.
This course is designed to provide learners with a comprehensive understanding of various neural network architectures used in NLP, including their differences, challenges, and practical use cases. By the end of the journey, participants will be equipped to confidently apply their knowledge in development or research environments.
You’ll learn to build and train neural networks for a wide range of linguistic processing tasks such as:
Text analysis & processing
Sentiment analysis
Language translation
Text summarization
And more advanced NLP applications
All using popular deep learning frameworks, and deploying them to the cloud while tuning for performance.
This is the ideal training for anyone looking to gain hands-on expertise in building intelligent language-driven solutions.
This LearningKit with more than 22 hours of learning is divided into three tracks:
Demo Natural Language Processing NLP Training
Course content
Track 1: Getting Started with Natural Language Processing
In this track, the focus will be on fundamentals of NLP, and text mining and analytics. Courses (8 hours +):
Natural Language Processing: Getting Started with NLP
Course: 40 Minutes
Course Overview
What is Natural Language Processing (NLP)
Building Blocks of Language
Syntactic and Semantic Analysis
Various Tasks of NLP
Heuristics-based NLP
Machine Learning-based NLP
Deep Learning-based NLP
Challenges with NLP
Tool Ecosystem of NLP
NLP Use Cases in Industry
Course Summary
Natural Language Processing: Linguistic Features Using NLTK & spaCy
Course: 1 Hour, 11 Minutes
Course Overview
Linguistic Features in Language Processing
Introduction to Natural Language Toolkit (NLTK)
Introduction to spaCy
spaCy verses NLTK
Using Linguistic Features in NLTK - Part 1
Using Linguistic Features in NLTK - Part 2
Types of spaCy Models3
Using Linguistic Features in spaCy - Part 1
Using Linguistic Features in spaCy - Part 2
Using Linguistic Features in spaCy - Part 3
Using Linguistic Features in spaCy - Part 4
Course Summary
Text Mining and Analytics: Pattern Matching & Information Extraction
Course: 1 Hour, 52 Minutes
Course Overview
A Heuristic Approach to NLP
WordNet Fundamentals
Performing Synonyms, Synset, and WordNet Hierarchy
Performing WordNet Relations and Semantic Similarity
Working with SentiWordNet and Sentiment Analysis
Working with Regex for Pattern Matching
Investigating Python Regex Language
Performing Basic NLTK Chunking and Regex
Performing Advanced NLTK Chunking and Regex
Modeling Movie Plot Sentiment Analysis with WordNet
Course Summary
Text Mining and Analytics: Machine Learning for Natural Language Processing
Course: 2 Hours, 3 Minutes
Course Overview
NLP with Machine Learning (ML)
Machine Learning Pipeline for NLP
Feature Engineering for NLP
Common ML Models Used in NLP
Predicting Sarcasm in Text: Data Loading
Predicting Sarcasm in Text: Data Analysis
Predicting Sarcasm in Text: Linguistic Features
Predicting Sarcasm in Text: Feature Engineering
Predicting Sarcasm in Text: Model Building Part 1
Predicting Sarcasm in Text: Model Building Part 2
Predicting Sarcasm in Text: Model Tuning
Course Summary
Text Mining and Analytics: Natural Language Processing Libraries
Course: 1 Hour, 59 Minutes
Course Overview
Introduction to Polyglot and TextBlob
Introduction to Gensim and CoreNLP
Using Basic Polyglot Features
Using Multi-language Part of Speech Tagging
Exploring Advanced PolyGlot Features
Implementing Basic TextBlob Features
Implementing Advanced TextBlob Features
Exploring Basic Gensim Features
Building bigram and trigram Using Gensim
Building an LDA Model for Topic Modeling
Exploring Advanced Gensim Features
Course Summary
Text Mining and Analytics: Hotel Reviews Sentiment Analysis
Course: 1 Hour, 8 Minutes
Course Overview
Loading Hotel Reviews Data
Installing Libraries and Data Loading
Utilizing Exploratory Data Analysis (EDA)
Exploring Linguistic Features of Data
Building NLP Models
Interpreting Model Tuning
Deploying AutoML, PyCaret, and Streamlit Models
NLP Project Best Practices
NLP Project Challenges and Deployment Strategies
Course Summary
Track 2: Natural Language Processing with Deep Learning
In this track, the focus will be on deep learning for NLP. Courses (9 hours +)
Deep Learning for NLP: Introduction
Course: 1 Hour, 18 Minutes
Course Overview
NLP with Deep Learning
NLP Use Cases in Deep Learning
Basic Deep Learning Frameworks
Intermediate Deep Learning Frameworks
Advanced Deep Learning Frameworks
Introduction to Sentiment Data
Using Deep Learning Pipelines for Sentiment Data
Sentiment Analysis - Overview & Data
Sentiment Analysis - EDA
Sentiment Analysis - Pre-processing
Sentiment Analysis - Modeling & Evaluation
Sentiment Analysis - Creating Accuracy & Loss Graphs
Course Summary
Deep Learning for NLP: Neural Network Architectures
Course: 2 Hours, 30 Minutes
Course Overview
Basic Architecture of a Neural Network
Multilayer Perceptron (MLP)
Recurrent Neural Network (RNN) Architecture
Challenges in RNN
Applications of Neural Network-based Architecture
Introducing the Product Reviews Data
Loading Product Reviews Data into Google Colaboratory
Understanding Product Reviews Data
Exploring Product Reviews Data
Pre-processing Product Reviews Data
Applying Feature Engineering - Word Representation
Creating Vector Representations Using Word2vec
Averaging Feature Vectors
Creating Word Embeddings with Word2Vec
Constructing a RNN Model with Word2vec Embeddings
Using GloVe Vectors
Product Reviews Classification Using RNN
Course Summary
Deep Learning for NLP: Memory-based Networks
Course: 1 Hour, 27 Minutes
Course Overview
Introduction to Memory-based Networks
Gated Recurrent Unit (GRU) Architecture
Long Short-term Memory (LSTM) Architecture
Fall of RNN versus Rise of LSTM
Variants of LSTM networks
Product Review Data Preparation for Modeling
Product Review Data Classification Using GRU
Product Review Data Classification Using LSTM
Product Review Data Classification Using Bi-LSTM
Result Comparison between RNN, GRU, and LSTM
Course Summary
Deep Learning for NLP: Transfer Learning
Course: 2 Hours, 10 Minutes
Course Overview
Introduction to Transfer Learning
Advantages and Challenges of Transfer Learning
Role of Language Modeling in Transfer Learning
Introduction to Basic Transfer Learning Models
Intermediate Transfer Learning Models
Advance Transfer Learning Models
Building ELMo Embedding Layer for Reviews
Creating ELMo an Model for Product Reviews
Classifying Product Reviews Using ELMo
Reshaping Data for the ELMo Embedding Layer
Building a Language Model Using ULMFiT
Implementing the Language Model Using ULMFiT
Classifying Product Reviews Using ULMFIT & FastText
Performing Result Comparison
Course Summary
Deep Learning for NLP: GitHub Bug Prediction Analysis
Course: 1 Hour, 56 Minutes
Course Overview
Case Study: Introduction to GitHub Bug Prediction
Case Study: Loading Data & Libraries
Case Study: Understanding the Data
Case Study: Basic Exploratory Data Analysis
Case Study: Punctuation & Stop Word Analysis
Case study: Advanced Data Preprocessing
Case Study: Data Cleaning
Case Study: Exploring Vectorization
Case Study: Exploring Embeddings
Case Study: Applying Deep Learning Modeling
Case Study: Performing Model Comparison
Course Summary
Track 3: Advanced NLP
In this track, the focus will be on transformer models, BERT, and GPT. Courses (4 hours +)
Advanced NLP: Introduction to Transformer Models
Course: 41 Minutes
Course Overview
Sequence-to-Sequence (Seq2Seq) Models
Attention in Seq2Seq Models
Transformer Architecture
Self-Attention Layer in Transformer Architecture
Multi-head Attention in Transformer Architecture
Transformer Encoder Block
Transformer Decoder Block
Transformer Model Architecture
Industry Use Cases for Transformer Models
Transformer Model Challenges
Course Summary
Advanced NLP: Introduction to BERT
Course: 1 Hour, 14 Minutes
Course Overview
BERT Architecture
Types of BERT Models
Transfer Learning with BERT
The Hugging Face Ecosystem
Practicing Model Setup & Data Exploration with BERT
Pre-processing Data with BERT
Using BERT for Sentiment Classification Training
Evaluating Models with BERT
Best Practices for BERT
BERT Challenges and Deployment Strategy
Course Summary
Advanced NLP: Introduction to GPT
Course: 1 Hour, 10 Minutes
Course Overview
Language Models
Generative Pre-trained Transformer (GPT)
GPT Versions
GPT-3 Model Architecture
GPT-3 Few-Shot Learning
GPT-3 Use Cases and Challenges
Downloading the GPT Model
Performing Greedy and Beam Searches in GPT
Performing Top K and Top P Sampling in GPT
Using Benchmark Prompts in GPT
Course Summary
Advanced NLP: Language Translation Using Transformer Model
Course: 1 Hour, 29 Minutes
Course Overview
Machine Translation
Using Single Sentence English to French Translation
Setting up the Environment for Translation
Performing EDA for Translation
Using Tokens and Vectors for Translation
Using Training and Validation Data for Translation
Using Transformer Encoder for Translation
Using Transformer Decoder for Translation
Defining Attention and Embedding for Translation
Assembling and Training the Model for Translation
Using a Trained Model for Translation
Course Summary
Track 4: NLP Case Studies
In this track, the focus will be on NLP case studies. Courses (1 hours +)
NLP Case Studies: News Scraping Translation & Summarization
Course: 43 Minutes
Course Overview
Text Summarization Application
Using Data Scraping
Performing Translation into English
Performing Text Summarization
Creating a User Interface (UI) with Gradio
Course Summary
NLP Case Studies: Article Text Comprehension & Question Answering
Course: 29 Minutes
Course Overview
The Q&A Pipeline and Text Comprehension
Installing PyTorch and Transformers Libraries
Importing a Text Comprehension Model
Using a Text Comprehension Model
Developing a Text Comprehension App Using Gradio
Course Summary
Assessment:
Final Exam: Natural Language Processing will test your knowledge and application of the topics presented throughout the Skillsoft Aspire Natural Language Processing Journey.
Specifications
Article number
137313022
SKU
137313022
Language
English
Qualifications of the Instructor
Certified
Course Format and Length
Teaching videos with subtitles, interactive elements and assignments and tests
Lesson duration
22 Hours
Assesments
The assessment tests your knowledge and application skills of the topics in the learning pathway. It is available 365 days after activation.
Online Virtuele labs
Receive 12 months of access to virtual labs corresponding to traditional course configuration. Active for 365 days after activation, availability varies by Training
Online mentor
You will have 24/7 access to an online mentor for all your specific technical questions on the study topic. The online mentor is available 365 days after activation, depending on the chosen Learning Kit.
Progress monitoring
Access to Material
365 days
Technical Requirements
Computer or mobile device, Stable internet connections Web browsersuch as Chrome, Firefox, Safari or Edge.
Support or Assistance
Helpdesk and online knowledge base 24/7
Certification
Certificate of participation in PDF format
Price and costs
Course price at no extra cost
Cancellation policy and money-back guarantee
We assess this on a case-by-case basis
Award Winning E-learning
Tip!
Provide a quiet learning environment, time and motivation, audio equipment such as headphones or speakers for audio, account information such as login details to access the e-learning platform.
Natural Language Processing (NLP) E-Learning Training Gecertificeerde docenten Q...
€239,58€198,00
Specifications
Article number
137313022
SKU
137313022
Language
English
Qualifications of the Instructor
Certified
Course Format and Length
Teaching videos with subtitles, interactive elements and assignments and tests
Lesson duration
22 Hours
Assesments
The assessment tests your knowledge and application skills of the topics in the learning pathway. It is available 365 days after activation.
Online Virtuele labs
Receive 12 months of access to virtual labs corresponding to traditional course configuration. Active for 365 days after activation, availability varies by Training
Online mentor
You will have 24/7 access to an online mentor for all your specific technical questions on the study topic. The online mentor is available 365 days after activation, depending on the chosen Learning Kit.
Progress monitoring
Access to Material
365 days
Technical Requirements
Computer or mobile device, Stable internet connections Web browsersuch as Chrome, Firefox, Safari or Edge.
Support or Assistance
Helpdesk and online knowledge base 24/7
Certification
Certificate of participation in PDF format
Price and costs
Course price at no extra cost
Cancellation policy and money-back guarantee
We assess this on a case-by-case basis
Award Winning E-learning
Tip!
Provide a quiet learning environment, time and motivation, audio equipment such as headphones or speakers for audio, account information such as login details to access the e-learning platform.
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