Natural Language Processing Training in Brussels, Belgium

20,453 Learners

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Course Overview

The Natural Language Processing course in Brussels covers concepts like statistical machine translation and neural models, deep semantic similarity models (DSSM), neural knowledge base embedding, deep reinforcement learning techniques, neural models applied in image captioning, and visual question answering with Python’s Natural Language Toolkit.

Key Features

Lifetime access to self-paced learning

Industry-recognized course completion certificate

Lifetime access to self-paced learning

Industry-recognized course completion certificate

Lifetime access to self-paced learning

Industry-recognized course completion certificate

Skills Covered

Perform text processing

Create an NLP module

Create a basic speech model

Working with NLP Pipeline

Classify cluster for articles

Convert speech to text

Perform text processing

Working with NLP Pipeline

Create an NLP module

Classify cluster for articles

Create a basic speech model

Convert speech to text

Perform text processing

Working with NLP Pipeline

Create an NLP module

Classify cluster for articles

Create a basic speech model

Convert speech to text

Begin your journey to success

Get lifetime access to self-paced e-learning content

View other cohorts

Benefits

The NLP market size is expected to reach $26.4 billion by 2024, with a growth rate of 21 percent from 2019 to 2024. Chatbots and virtual assistants have effectively used NLP—the increasing use of smart devices and cloud-based solutions will drive this growth.

Designation
Annual Salary
Hiring Companies

NLP Engineer
Annual Salary
$78KMin
$114KAverage
$150KMax
Source: Glassdoor
Hiring Companies
Source: Indeed
Data Scientist
Annual Salary
$83KMin
$113KAverage
$154KMax
Source: Glassdoor
Hiring Companies
Source: Indeed
Data Analyst
Annual Salary
$43KMin
$62KAverage
$95KMax
Source: Glassdoor
Hiring Companies
Source: Indeed
Analytics Manager
Annual Salary
$70KMin
$97KAverage
$139KMax
Source: Glassdoor
Hiring Companies
Source: Indeed

Training Options

online Bootcamp

Flexi Pass Enabled: Flexibility to reschedule your cohort within first 90 days of access.

Lifetime access to high-quality self-paced e-learning content and live class recordings
24x7 learner assistance and support

Batch starting in Brussels from:

22^nd Feb, Weekend Class

View All Schedules

€855

Corporate Training

Upskill or reskill your teams

Flexible pricing & billing options
Private cohorts available
Training progress dashboards
Skills assessment & benchmarking
Platform integration capabilities
Dedicated customer success manager

Know More

Course Curriculum

Eligibility

Natural Language Processing course in Brussels, Belgium 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 in Brussels, Belgium should have a basic understanding of math, statistics, data science, and machine learning.

Course Content

Section 01 - NLP Overview (Self Learning)
Preview
- Lesson 01: Natural Language Processing - Introduction
  19:51Preview
  - 1.01 Introduction
    19:51
- Lesson 02: Natural Language
  13:47Preview
  - 2.01 Natural Language
    13:47
- Lesson 03: Corpus
  10:28Preview
  - 3.01 Corpus
    10:28
- Lesson 04: Text Analytics
  15:29
  - 4.01 Text Analytics
    15:29
- Lesson 05: Feature Extraction
  17:38Preview
  - 5.01 Feature Extraction
    17:38
- Lesson 06: Machine Learning
  25:09
  - 6.01 Machine Learning
    25:09
- Lesson 07: Python Toolkits
  19:03
  - 7.01 Python Toolkits
    19:03
- Lesson 08: Bagging
  05:19
  - 8.01 Bagging
    05:19
- Lesson 09: Deep Learning
  33:24Preview
  - 9.01 Deep Learning: Part One
    16:33
  - 9.02 Deep Learning: Part Two
    16:51
- Lesson 10: Demonstrations
  02:52:37Preview
  - 10.01 Demo - Setup
    11:50
  - 10.02 Demo - RNN One: Build Model
    18:06
  - 10.03 Demo - RNN Two: Analyze Results
    12:00
  - 10.04 Demo - Clustering and Topic Modeling: Part One
    20:27
  - 10.05 Demo - Clustering and Topic Modeling: Part Two
    21:47
  - 10.06 Demo - Sentiment Analysis
    21:06
  - 10.07 Demo - NLTK
    12:48
  - 10.08 Demo - sPacy
    23:38
  - 10.09 Demo - Glove
    12:42
  - 10.10 Demo - SpamHam
    18:13

Section 02 - Live Class Curriculum
Preview
- Introduction to NLP
  - Definition and scope of NLP
  - Real-world applications and significance of NLP
  - Basic terminologies such as corpus, tokenization, and syntactic analysis
- 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
  - Assisted Practices : Implementation of twitter data sentiment analysis using NLTK Naive Bayes Classifier
  - Lesson-end project
- NLP Text Vectorization
  - Vector Representation of Text - One Hot Encoding
  - Understanding BoW Technique
  - TFIDF
  - Assisted Practices
  - Lesson-end project
- Distributed Representations
  - Word embeddings and their importance in NLP
  - Detailed explanation of Word2Vec and Glove embeddings.
  - Training and using pre-trained word embeddings.
  - Assisted Practices
  - Lesson-end project
- Machine Translation and Document Search
  - Machine translation systems and their applications.
  - Building a basic machine translation system.
  - Introduction to document search using methods like TF-IDF and BM25.
  - Evaluation metrics for machine translation and information retrieval.
  - Assisted Practices
  - Lesson-end project
- Sequence Models
  - Introduction to sequence modeling in NLP
  - Recurrent Neural Networks (RNNs) and their applications in NLP tasks
  - Applications of sequence models in sentiment analysis
  - Challenges in training RNNs, such as vanishing gradients
  - Assisted Practices using RNNs for NLP tasks
  - Lesson-end project
- 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.
  - Assisted Practices
  - Lesson-end project
- Introduction to Audio Analytics
  - Important Definitions
  - Overview of the Python ecosystem for audio analytics
  - Introduction to audio file formats (WAV, MP3, etc.)
  - Reading and playing audio files using Python libraries (Librosa, PyDub)
  - Load, visualize, and manipulate audio data
  - Extract basic features (e.g., amplitude, duration)
  - Assisted Practices
  - Lesson-end project
- Digital Signal Processing and Feature Extraction
  - Basics of signal processing (convolution, Fourier Transform)
  - Frequency domain analysis using Python
  - Introduction to MFCCs and other spectral features
  - Implementation of feature extraction in Python
  - Extract and visualize MFCCs from audio data
  - Compare different feature extraction techniques
  - Assisted Practices
  - Lesson-end project
- Deep Learning for speech
  - Applications of machine learning in audio
  - Building Deep Learning Models for Speech Recognition
  - Transfer Learning for Speech Recognition
  - Assisted Practices
  - Lesson-end project
- Audio Synthesis and Generation 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
  - Assisted Practices
  - Lesson-end project

Natural Language Processing
Preview
- Section 03 - Practice Projects
  - Twitter Hate
  - Zomato Rating

Programme Syllabus

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Exam & Certification

Who provides the certificate and how long it is valid for?
Once you successfully complete the Natural Language Processing course in Brussels, Belgium as part of Artificial Intelligence Engineer Master’s Program, Simplilearn will provide you with an industry-recognized course completion certificate which will have a lifelong validity.
What do I need to do to unlock my Simplilearn certificate?
You must complete this Natural Language Processing course in order to unlock the Simplilearn certificate.

Why Join this Program

Develop skills for real career growthCutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills
Learn from experts active in their field, not out-of-touch trainersLeading practitioners who bring current best practices and case studies to sessions that fit into your work schedule.
Learn by working on real-world problemsCapstone projects involving real world data sets with virtual labs for hands-on learning
Structured guidance ensuring learning never stops24x7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts

FAQs

What is Natural Language Processing (NLP)?
Natural language processing (or text analytics/text mining) applies analytic tools to read from a huge collection of natural language data to derive a meaningful conclusion. It has given rise to chatbots and virtual assistants to address queries of millions of users. It is a branch of artificial intelligence that has important implications on the ways that computers and humans interact. Natural language processing will help bridge the gap between human communication and digital data.
How do I become an NLP Engineer?
This course in Brussels, Belgium will give you a complete overview of Natural Language Processing, enough to prepare you for a lucrative career as a Natural Language Processing Engineer. You will get Simplilearn’s NLP certification that will be a testament to your new skills and on-the-job expertise.
How do beginners learn Natural Language Processing?
Beginners can learn Natural Language Processing through this NLP certification course. This certification course in Brussels, Belgium covers concepts like statistical machine translation and neural models, deep semantic similarity models (DSSM), neural knowledge base embedding, deep reinforcement learning techniques, neural models applied in image captioning, and visual question answering with Python’s Natural Language Toolkit, which can help a beginner learn everything about NLP.
How do I enroll in this Natural Language Processing course in Brussels, Belgium?
You can enroll in this Natural Language Processing course in Brussels, Belgium on our website and make an online payment using any of the following options:
- Visa Credit or Debit Card
- MasterCard
- American Express
- Diner’s Club
- PayPal
Once payment is received you will automatically receive a payment receipt and access information via email.
Who are the instructors and how they are selected?
All of Simplilearn’s Natural Language Processing trainers are experienced industry experts. Each of them have gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We at Simplilearn also ensure that only those trainers with a high alumni rating are selected as our faculty.
What is online classroom training?
Online classroom training for the Natural Language Processing certification is conducted via live streaming. The classes are conducted by an NLP certified trainer with more than 15 years of work and training experience to ensure that you learn everything about NLP.
Is this live training, or will I watch pre-recorded videos?
Natural Language Processing course is conducted through live streaming. They are interactive sessions that enable you to ask questions and participate in discussions during class time. We do, however, provide recordings of each session you attend for your future reference. Classes are attended by a global audience to enrich your learning experience.
Can I cancel my enrollment? Will I get a refund?
Yes, you can cancel your enrollment if necessary. We will refund the course price after deducting an administration fee. To learn more, you can view our refund policy.
What is covered under the 24/7 Support promise?
We offer 24/7 support through email, chat, and calls. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your course with us.
What is Global Teaching Assistance?
All of Simplilearn’s teaching assistants are subject matter experts who will help you learn Natural Language Processing thoroughly and get certified in the first attempt itself. They engage students proactively to ensure the course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance. Teaching Assistance is available during business hours.
Whom should I contact to learn more about this Natural Language Processing course?
If you want to know more about this NLP certification course, you can contact us using the form on the right of any page on the Simplilearn website, or select the Live Chat link in the live chat tab present in all the pages at the bottom right part of the webpage. Our customer service representatives will be able to give you more details.
What is the recommended learning path after completing Natural Language Processing certification course?
You can either enroll in our Artificial Intelligence certification course or if you are looking to get the University certificate, you can enroll in the Post Graduate Program in AI and Machine Learning.

Acknowledgement
PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, OPM3 and the PMI ATP seal are the registered marks of the Project Management Institute, Inc.

Natural Language Processing Training in Brussels, Belgium

Course Overview

Key Features

Skills Covered

Begin your journey to success

Benefits

Training Options

online Bootcamp

Corporate Training

Course Curriculum

Eligibility

Pre-requisites

Course Content

Section 01 - NLP Overview (Self Learning)

Lesson 01: Natural Language Processing - Introduction

1.01 Introduction

Lesson 02: Natural Language

2.01 Natural Language

Lesson 03: Corpus

3.01 Corpus

Lesson 04: Text Analytics

4.01 Text Analytics

Lesson 05: Feature Extraction

5.01 Feature Extraction

Lesson 06: Machine Learning

6.01 Machine Learning

Lesson 07: Python Toolkits

7.01 Python Toolkits

Lesson 08: Bagging

8.01 Bagging

Lesson 09: Deep Learning

9.01 Deep Learning: Part One

9.02 Deep Learning: Part Two

Lesson 10: Demonstrations

10.01 Demo - Setup

10.02 Demo - RNN One: Build Model

10.03 Demo - RNN Two: Analyze Results

10.04 Demo - Clustering and Topic Modeling: Part One

10.05 Demo - Clustering and Topic Modeling: Part Two

10.06 Demo - Sentiment Analysis

10.07 Demo - NLTK

10.08 Demo - sPacy

10.09 Demo - Glove

10.10 Demo - SpamHam

Section 02 - Live Class Curriculum

Introduction to NLP

Definition and scope of NLP

Real-world applications and significance of NLP

Basic terminologies such as corpus, tokenization, and syntactic analysis

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

Assisted Practices : Implementation of twitter data sentiment analysis using NLTK Naive Bayes Classifier

Lesson-end project

NLP Text Vectorization

Vector Representation of Text - One Hot Encoding

Understanding BoW Technique

TFIDF

Assisted Practices

Lesson-end project

Distributed Representations

Word embeddings and their importance in NLP

Detailed explanation of Word2Vec and Glove embeddings.

Training and using pre-trained word embeddings.

Assisted Practices

Lesson-end project

Machine Translation and Document Search

Machine translation systems and their applications.

Building a basic machine translation system.

Introduction to document search using methods like TF-IDF and BM25.

Evaluation metrics for machine translation and information retrieval.

Assisted Practices

Lesson-end project

Sequence Models

Introduction to sequence modeling in NLP

Recurrent Neural Networks (RNNs) and their applications in NLP tasks

Applications of sequence models in sentiment analysis

Challenges in training RNNs, such as vanishing gradients