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LLMs for Sentiment Analysis

21 Hours
Pelatihan langsung yang dipimpin instruktur di Indonesia (online atau di lokasi) ditujukan untuk profesional data dan pemasaran tingkat menengah yang ingin menerapkan LLM untuk menganalisis dan menafsirkan sentimen publik dari berbagai sumber teks seperti postingan media sosial, ulasan produk, dan umpan balik pelanggan. Pada akhir pelatihan ini, peserta akan mampu:
  • Memahami prinsip analisis sentimen dan penerapannya menggunakan LLM.
  • Praproses dan siapkan kumpulan data untuk analisis sentimen.
  • Latih dan sempurnakan LLM untuk mencerminkan sentimen dalam teks secara akurat.
  • Analisis sentimen secara real-time dari media sosial dan sumber teks lainnya.
  • Integrasikan temuan analisis sentimen ke dalam strategi bisnis dan proses pengambilan keputusan.
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Advanced LLMs for NLP Tasks

21 Hours
Large language models (LLMs) are AI models that can process and generate large amounts of natural language data, such as text, speech, and audio. LLMs can learn the patterns and structure of their input training data and then generate new data that has similar characteristics. LLMs can also perform various natural language processing (NLP) tasks, such as natural language understanding (NLU), natural language inference (NLI), knowledge graph construction and completion, commonsense reasoning, dialogue generation and management, and multimodal generation and understanding.This instructor-led, live training (online or onsite) is aimed at intermediate-level data scientists, AI developers, and AI enthusiasts who wish to use LLMs to perform various NLP tasks and create novel and diverse content for different purposes.By the end of this training, participants will be able to:
  • Establish a development environment with LLMs and essential tools.
  • Expertly perform NLU and NLI tasks with LLMs.
  • Extract, infer, and utilize knowledge graphs effectively.
  • Generate and manage dialogues using LLMs for conversational applications.
  • Evaluate content quality and diversity generated by LLMs and generative AI.
  • Apply ethical principles, ensuring fairness and responsible use of LLMs.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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Exploring Generative Pre-trained Transformers (GPT): From GPT-3 to GPT-4

14 Hours
Generative Pre-trained Transformers (GPT) adalah model tercanggih dalam pemrosesan bahasa alami yang telah merevolusi berbagai aplikasi, termasuk pembuatan bahasa, penyelesaian teks, dan terjemahan mesin. Kursus ini memberikan eksplorasi mendalam tentang model GPT, dengan fokus pada GPT-3 dan kemajuan terbaru dalam GPT-4. Peserta akan mendapatkan wawasan tentang arsitektur, teknik pelatihan, dan penerapan model GPT.Pelatihan langsung yang dipandu instruktur (online atau di tempat) ini ditujukan untuk ilmuwan data, insinyur pembelajaran mesin, peneliti NLP, dan penggemar AI yang ingin memahami cara kerja bagian dalam model GPT, mengeksplorasi kemampuan GPT-3 dan GPT-4 , dan pelajari cara memanfaatkan model ini untuk tugas NLP mereka.Pada akhir pelatihan ini, peserta akan dapat:
    Pahami konsep dan prinsip utama di balik Generative Pre-trained Transformers. Pahami arsitektur dan proses pelatihan model GPT. Manfaatkan GPT-3 untuk tugas-tugas seperti pembuatan teks, penyelesaian, dan terjemahan. Jelajahi kemajuan terbaru dalam GPT-4 dan potensi aplikasinya. Terapkan model GPT ke proyek dan tugas NLP mereka sendiri.
Format Kursus
    Kuliah dan diskusi interaktif. Banyak latihan dan latihan. Implementasi langsung di lingkungan lab langsung.
Opsi Penyesuaian Kursus
    Untuk meminta pelatihan khusus untuk kursus ini, silakan hubungi kami untuk mengaturnya.
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Hugging Face for Natural Language Processing (NLP)

14 Hours
Hugging Face is a powerful open-source library and platform for natural language processing (NLP).  This instructor-led, live training (online or onsite) is aimed at data scientists, machine learning practitioners, and NLP researchers and enthusiasts who wish to effectively utilize Hugging Face for NLP tasks. By the end of this training, participants will be able to:
  • Utilize a Hugging Face Transformer model, and fine-tune it on a specific dataset.
  • Gain the ability to independently address common NLP challenges.
  • Create and share your model demos effectively.
  • Streamline the optimization of your models for production.
  • Employ Hugging Face Transformers for solving a wide range of machine learning problems.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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NLP: Natural Language Processing with R

21 Hours
It is estimated that unstructured data accounts for more than 90 percent of all data, much of it in the form of text. Blog posts, tweets, social media, and other digital publications continuously add to this growing body of data. This instructor-led, live course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data. Data samples are available in various languages per customer requirements. By the end of this training participants will be able to prepare data sets (large and small) from disparate sources, then apply the right algorithms to analyze and report on its significance.
Format of the Course
  • Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding
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Natural Language Processing (NLP) with Python

28 Hours
Kursus ini memperkenalkan linguist atau programmer untuk NLP dalam Python. Selama kursus ini kami akan sebagian besar menggunakan nltk.org (Natural Language Tool Kit), tetapi kami juga akan menggunakan perpustakaan lain yang relevan dan berguna untuk NLP. Saat ini kita dapat menjalankan kursus ini dalam Python 2.x atau Python 3.x. Contohnya dalam bahasa Inggris atau Mandarin (普通话). Bahasa lain juga dapat tersedia jika disetujui sebelum pemesanan.
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Artificial Intelligence (AI) Overview

7 Hours
Kursus ini telah diciptakan untuk manajer, arsitek solusi, pejabat inovasi, CTO, arsitek perangkat lunak dan siapa saja yang tertarik dengan gambaran keseluruhan kecerdasan buatan yang diterapkan dan ramalan terdekat untuk pengembangan.
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Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP

21 Hours
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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Building Chatbots in Python

21 Hours
ChatBots are computer programs that automatically simulate human responses via chat interfaces. ChatBots help organizations maximize their operations efficiency by providing easier and faster options for their user interactions. In this instructor-led, live training, participants will learn how to build chatbots in Python. By the end of this training, participants will be able to:
  • Understand the fundamentals of building chatbots
  • Build, test, deploy, and troubleshoot various chatbots using Python
Audience
  • Developers
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
Note
  • To request a customized training for this course, please contact us to arrange.
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Deep Learning for NLP (Natural Language Processing)

28 Hours
DL (Deep Learning) is a subset of ML (Machine Learning). Python is a popular programming language that contains libraries for Deep Learning for NLP. Deep Learning for NLP (Natural Language Processing) allows a machine to learn simple to complex language processing. Among the tasks currently possible are language translation and caption generation for photos. In this instructor-led, live training, participants will learn to use Python libraries for NLP as they create an application that processes a set of pictures and generates captions.  By the end of this training, participants will be able to:
  • Design and code DL for NLP using Python libraries.
  • Create Python code that reads a substantially huge collection of pictures and generates keywords.
  • Create Python Code that generates captions from the detected keywords.
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Python for Natural Language Generation

21 Hours
Natural language generation (NLG) refers to the production of natural language text or speech by a computer. In this instructor-led, live training, participants will learn how to use Python to produce high-quality natural language text by building their own NLG system from scratch. Case studies will also be examined and the relevant concepts will be applied to live lab projects for generating content. By the end of this training, participants will be able to:
  • Use NLG to automatically generate content for various industries, from journalism, to real estate, to weather and sports reporting
  • Select and organize source content, plan sentences, and prepare a system for automatic generation of original content
  • Understand the NLG pipeline and apply the right techniques at each stage
  • Understand the architecture of a Natural Language Generation (NLG) system
  • Implement the most suitable algorithms and models for analysis and ordering
  • Pull data from publicly available data sources as well as curated databases to use as material for generated text
  • Replace manual and laborious writing processes with computer-generated, automated content creation
Audience
  • Developers
  • Data scientists
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Natural Language Processing (NLP) - AI/Robotics

21 Hours
This classroom based training session will explore NLP techniques in conjunction with the application of AI and Robotics in business. Delegates will undertake computer based examples and case study solving exercises using Python
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OpenNLP for Text Based Machine Learning

14 Hours
The Apache OpenNLP library is a machine learning based toolkit for processing natural language text. It supports the most common NLP tasks, such as language detection, tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing and coreference resolution. In this instructor-led, live training, participants will learn how to create models for processing text based data using OpenNLP. Sample training data as well customized data sets will be used as the basis for the lab exercises. By the end of this training, participants will be able to:
  • Install and configure OpenNLP
  • Download existing models as well as create their own
  • Train the models on various sets of sample data
  • Integrate OpenNLP with existing Java applications
Audience
  • Developers
  • Data scientists
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Natural Language Processing (NLP) with Deep Dive in Python and NLTK

35 Hours
By the end of the training the delegates are expected to be sufficiently equipped with the essential python concepts and should be able to sufficiently use NLTK to implement most of the NLP and ML based operations. The training is aimed at giving not just an executional knowledge but also the logical and operational knowledge of the technology therein.  
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Natural Language Processing (NLP) with Python spaCy

14 Hours
This instructor-led, live training (online or onsite) is aimed at developers and data scientists who wish to use spaCy to process very large volumes of text to find patterns and gain insights. By the end of this training, participants will be able to:
  • Install and configure spaCy.
  • Understand spaCy's approach to Natural Language Processing (NLP).
  • Extract patterns and obtain business insights from large-scale data sources.
  • Integrate the spaCy library with existing web and legacy applications.
  • Deploy spaCy to live production environments to predict human behavior.
  • Use spaCy to pre-process text for Deep Learning
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
  • To learn more about spaCy, please visit: https://spacy.io/
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Text Summarization with Python

14 Hours
In Python Machine Learning, the Text Summarization feature is able to read the input text and produce a text summary. This capability is available from the command-line or as a Python API/Library. One exciting application is the rapid creation of executive summaries; this is particularly useful for organizations that need to review large bodies of text data before generating reports and presentations. In this instructor-led, live training, participants will learn to use Python to create a simple application that auto-generates a summary of input text. By the end of this training, participants will be able to:
  • Use a command-line tool that summarizes text.
  • Design and create Text Summarization code using Python libraries.
  • Evaluate three Python summarization libraries: sumy 0.7.0, pysummarization 1.0.4, readless 1.0.17
Audience
  • Developers
  • Data Scientists
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Natural Language Processing (NLP) with TensorFlow

35 Hours
TensorFlow™ is an open source software library for numerical computation using data flow graphs. SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow. Word2Vec is used for learning vector representations of words, called "word embeddings". Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.). Used in tandem, SyntaxNet and Word2Vec allows users to generate Learned Embedding models from Natural Language input. Audience This course is targeted at Developers and engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs. After completing this course, delegates will:
  • understand TensorFlow’s structure and deployment mechanisms
  • be able to carry out installation / production environment / architecture tasks and configuration
  • be able to assess code quality, perform debugging, monitoring
  • be able to implement advanced production like training models, embedding terms, building graphs and logging
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NLP with Deeplearning4j

14 Hours
Deeplearning4j is an open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs. Word2Vec is a method of computing vector representations of words introduced by a team of researchers at Google led by Tomas Mikolov. Audience This course is directed at researchers, engineers and developers seeking to utilize Deeplearning4J to construct Word2Vec models.
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Natural Language Processing (NLP)

21 Hours
This course has been designed for people interested in extracting meaning from written English text, though the knowledge can be applied to other human languages as well. The course will cover how to make use of text written by humans, such as  blog posts, tweets, etc... For example, an analyst can set up an algorithm which will reach a conclusion automatically based on extensive data source.
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Scaling Data Pipelines with Spark NLP

14 Hours
Spark NLP is an open source library, built on Apache Spark, for natural language processing with Python, Java, and Scala. It is widely used for enterprise and industry verticals, such as healthcare, finance, life science, and recruiting. This instructor-led, live training (online or onsite) is aimed at data scientists and developers who wish to use Spark NLP, built on top of Apache Spark, to develop, implement, and scale natural language text processing models and pipelines. By the end of this training, participants will be able to:
  • Set up the necessary development environment to start building NLP pipelines with Spark NLP.
  • Understand the features, architecture, and benefits of using Spark NLP.
  • Use the pre-trained models available in Spark NLP to implement text processing.
  • Learn how to build, train, and scale Spark NLP models for production-grade projects.
  • Apply classification, inference, and sentiment analysis on real-world use cases (clinical data, customer behavior insights, etc.).
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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NLP with Python and TextBlob

14 Hours
TextBlob is a Python NLP library for processing textual data. It provides a simple API that makes it easy to perform NLP tasks, such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, etc. This instructor-led, live training (online or onsite) is aimed at data scientists and developers who wish to use TextBlob to implement and simplify NLP tasks, such as sentiment analysis, spelling corrections, text classification modeling, etc. By the end of this training, participants will be able to:
  • Set up the necessary development environment to start implementing NLP tasks with TextBlob.
  • Understand the features, architecture, and advantages of TextBlob.
  • Learn how to build text classification systems using TextBlob.
  • Perform common NLP tasks (Tokenization, WordNet, Sentiment analysis, Spelling correction, etc.)
  • Execute advanced implementations with simple APIs and a few lines of codes.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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Testimonials(4)

This is one of the best hands-on with exercises programming courses I have ever taken.

Laura Kahn
Course - Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP

I did like the exercises

Office for National Statistics
Course - Natural Language Processing with Python

Exercises and exchanges during questions/answers

Antoine - Physiobotic
Course - Scaling Data Pipelines with Spark NLP

The good humor, support and skills of the trainer.

Oumayma - Physiobotic
Course - Scaling Data Pipelines with Spark NLP

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