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    • Large Language Models

    Large Language Models (LLMs) Courses Online

    Study large language models for advanced natural language processing. Learn to build and apply models like GPT-3 for various NLP tasks.

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    Explore the Large Language Models (LLMs) Course Catalog

    • Status: Free Trial
      Free Trial
      D

      Duke University

      MLOps Platforms: Amazon SageMaker and Azure ML

      Skills you'll gain: AWS SageMaker, MLOps (Machine Learning Operations), Microsoft Azure, Exploratory Data Analysis, Data Pipelines, Amazon Web Services, Feature Engineering, Cloud Solutions, Artificial Intelligence and Machine Learning (AI/ML), Data Analysis, Predictive Modeling, Serverless Computing, Machine Learning, Machine Learning Algorithms, Containerization

      3.6
      Rating, 3.6 out of 5 stars
      ·
      48 reviews

      Advanced · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      E

      EDUCBA

      ARM Cortex (STM32) Deep Dive: Mastering Embedded Systems

      Skills you'll gain: Embedded Software, Embedded Systems, Peripheral Devices, C (Programming Language), System Programming, Hardware Architecture, Data Structures, Debugging, Hardware Troubleshooting, Development Environment, Performance Tuning, Integrated Development Environments, C++ (Programming Language)

      4.7
      Rating, 4.7 out of 5 stars
      ·
      42 reviews

      Advanced · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      I

      IBM

      AI Workflow: Machine Learning, Visual Recognition and NLP

      Skills you'll gain: Tensorflow, Supervised Learning, Artificial Neural Networks, Regression Analysis, Classification And Regression Tree (CART), Applied Machine Learning, Machine Learning Algorithms, Deep Learning, IBM Cloud, Image Analysis, Machine Learning, Random Forest Algorithm, Artificial Intelligence and Machine Learning (AI/ML), Predictive Modeling, Decision Tree Learning, Natural Language Processing, Feature Engineering, Scikit Learn (Machine Learning Library), Business Metrics, Computer Vision

      4.4
      Rating, 4.4 out of 5 stars
      ·
      86 reviews

      Advanced · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      G

      Google

      Go Beyond the Numbers: Translate Data into Insights

      Skills you'll gain: Exploratory Data Analysis, Data Storytelling, Data Visualization Software, Data Presentation, Data Transformation, Data Ethics, Tableau Software, Data Manipulation, Data Cleansing, Data Analysis, Stakeholder Communications, Matplotlib, Pandas (Python Package), Jupyter, Data Validation, Python Programming

      4.7
      Rating, 4.7 out of 5 stars
      ·
      797 reviews

      Advanced · Course · 1 - 3 Months

    • É

      École Polytechnique Fédérale de Lausanne

      Interest Rate Models

      Skills you'll gain: Derivatives, Financial Market, Risk Management, Investments, Financial Modeling, Market Data, Mathematical Modeling, Finance, Probability, Statistical Modeling, Estimation, Applied Mathematics

      4.5
      Rating, 4.5 out of 5 stars
      ·
      193 reviews

      Advanced · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      I

      IBM

      AI Workflow: AI in Production

      Skills you'll gain: Application Deployment, MLOps (Machine Learning Operations), Unit Testing, Docker (Software), Containerization, Kubernetes, IBM Cloud, User Feedback, Performance Analysis, Project Performance, Business Metrics, Natural Language Processing, Project Implementation, Time Series Analysis and Forecasting, Continuous Monitoring

      4.5
      Rating, 4.5 out of 5 stars
      ·
      50 reviews

      Advanced · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      G

      Google

      Google Advanced Data Analytics Capstone

      Skills you'll gain: Regression Analysis, Data Visualization, Statistical Analysis, Advanced Analytics, Data Analysis, Business Analytics, Data-Driven Decision-Making, Predictive Modeling, Machine Learning Methods, Interviewing Skills, Portfolio Management

      4.9
      Rating, 4.9 out of 5 stars
      ·
      1.2K reviews

      Advanced · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      S

      Stanford University

      Probabilistic Graphical Models 2: Inference

      Skills you'll gain: Bayesian Network, Bayesian Statistics, Statistical Inference, Markov Model, Graph Theory, Sampling (Statistics), Applied Machine Learning, Statistical Methods, Probability & Statistics, Algorithms, Probability Distribution, Machine Learning Algorithms, Computational Thinking

      4.6
      Rating, 4.6 out of 5 stars
      ·
      488 reviews

      Advanced · Course · 1 - 3 Months

    • L

      LearnQuest

      Developing AI Applications on Azure

      Skills you'll gain: Artificial Intelligence and Machine Learning (AI/ML), Microsoft Azure, Applied Machine Learning, Python Programming, Machine Learning, Machine Learning Algorithms, Data Science, Image Analysis, Computer Vision, Application Deployment, Natural Language Processing, Application Programming Interface (API)

      4.4
      Rating, 4.4 out of 5 stars
      ·
      977 reviews

      Advanced · Course · 1 - 3 Months

    • S

      Stanford University

      Social and Economic Networks: Models and Analysis

      Skills you'll gain: Network Analysis, Network Model, Social Sciences, Sociology, Economics, Policy, and Social Studies, Game Theory, Behavioral Economics, Graph Theory, Mathematical Modeling, Markov Model, Probability & Statistics, Probability Distribution, Bayesian Statistics, Simulations

      4.8
      Rating, 4.8 out of 5 stars
      ·
      754 reviews

      Advanced · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      J

      Johns Hopkins University

      Advanced Linear Models for Data Science 2: Statistical Linear Models

      Skills you'll gain: Regression Analysis, Linear Algebra, R Programming, Probability Distribution, Statistical Modeling, Mathematical Modeling, Probability & Statistics, Applied Mathematics, Statistical Analysis, Integral Calculus

      4.6
      Rating, 4.6 out of 5 stars
      ·
      101 reviews

      Advanced · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      D

      Duke University

      Spark, Hadoop, and Snowflake for Data Engineering

      Skills you'll gain: PySpark, Snowflake Schema, Databricks, Data Pipelines, Apache Spark, MLOps (Machine Learning Operations), Apache Hadoop, Big Data, Data Warehousing, Data Quality, Data Integration, Data Processing, DevOps, Data Transformation, SQL

      3.8
      Rating, 3.8 out of 5 stars
      ·
      52 reviews

      Advanced · Course · 1 - 4 Weeks

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    In summary, here are 10 of our most popular large language models courses

    • MLOps Platforms: Amazon SageMaker and Azure ML: Duke University
    • ARM Cortex (STM32) Deep Dive: Mastering Embedded Systems: EDUCBA
    • AI Workflow: Machine Learning, Visual Recognition and NLP: IBM
    • Go Beyond the Numbers: Translate Data into Insights: Google
    • Interest Rate Models: École Polytechnique Fédérale de Lausanne
    • AI Workflow: AI in Production: IBM
    • Google Advanced Data Analytics Capstone: Google
    • Probabilistic Graphical Models 2: Inference: Stanford University
    • Developing AI Applications on Azure: LearnQuest
    • Social and Economic Networks: Models and Analysis: Stanford University

    Frequently Asked Questions about Large Language Models

    Coursera’s courses on Large Language Models offer learners insights and skills related to cutting-edge AI technologies:

    • Fundamental concepts of natural language processing (NLP) and how LLMs are developed and utilized.
    • Techniques for training, fine-tuning, and deploying LLMs such as GPT (Generative Pre-trained Transformer).
    • Understanding the ethical considerations and societal impacts of LLMs.
    • Skills in integrating LLMs with various applications to enhance user interaction and automate tasks.
    • Best practices for managing data privacy and security when using LLMs.
    • Exploration of recent advancements and potential future developments in LLM technology.‎

    Courses on LLMs are accessible to learners with diverse backgrounds:

    • Beginners with an interest in AI and machine learning can start with introductory courses that provide a basic understanding of NLP and LLMs.
    • Those with some technical experience can enhance their knowledge with courses focused on specific aspects of LLM development and deployment.
    • Advanced learners can engage with complex topics related to the cutting-edge applications and ethical implications of LLMs.‎

    Upon completing LLM courses, learners can obtain credentials:

    • Professional certificates that indicate participation in courses focused on LLMs and their applications.
    • Specializations that cover technical, ethical, and practical aspects of LLMs, aimed at broadening understanding and application skills.
    • Certificates that learners may include in their portfolios to reflect their engagement with advanced AI concepts.‎

    Skills in LLMs can open up various career opportunities in the tech industry, particularly in roles focused on AI and machine learning:

    • AI Research Scientist, working on the development of new language models and NLP techniques.
    • Machine Learning Engineer, implementing and scaling LLMs for commercial or research applications.
    • Data Scientist, leveraging LLMs for data analysis and insight generation.
    • NLP Specialist, focusing on improving interaction between humans and computers through language.
    • Ethical AI Advocate, analyzing and addressing the societal impacts of AI technologies like LLMs.‎

    Looking to enhance your team's LLM skills? Coursera provides tailored enterprise solutions for teams ranging of 5-125 employees. Our offerings include advanced analytics, customized learning paths, and collaborative tools. To explore our LLM training options and make a purchase, please visit our Coursera for Teams page.‎

    This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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