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    • Keras

    Keras Courses Online

    Explore Keras for deep learning model development. Learn to build and train neural networks using this high-level API for TensorFlow.

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    Explore the Keras Course Catalog

    • G

      Google Cloud

      Machine Learning with TensorFlow on Google Cloud en Français

      Skills you'll gain: Feature Engineering, Exploratory Data Analysis, Tensorflow, Data Analysis, Keras (Neural Network Library), Google Cloud Platform, Applied Machine Learning, Data Quality, Data Cleansing, Data Strategy, Supervised Learning, Machine Learning, Performance Tuning, Artificial Intelligence and Machine Learning (AI/ML), MLOps (Machine Learning Operations), Dataflow, Deep Learning, Machine Learning Algorithms, Data Processing, Artificial Neural Networks

      4.2
      Rating, 4.2 out of 5 stars
      ·
      30 reviews

      Intermediate · Specialization · 3 - 6 Months

    • C

      Coursera Project Network

      Medical Insurance Premium Prediction with Machine Learning

      Skills you'll gain: Data Visualization, Keras (Neural Network Library), Artificial Neural Networks, Interactive Data Visualization, Predictive Modeling, Tensorflow, Applied Machine Learning, Feature Engineering, Data Processing, Predictive Analytics, Data Manipulation, Data Cleansing, Machine Learning, Regression Analysis

      4.7
      Rating, 4.7 out of 5 stars
      ·
      23 reviews

      Beginner · Guided Project · Less Than 2 Hours

    • Status: New
      New
      U

      Universidades Anáhuac

      Deep Learning: Del Concepto a la Práctica

      Skills you'll gain: Artificial Neural Networks, Keras (Neural Network Library), Deep Learning, Applied Machine Learning, Tensorflow, Image Analysis, Computer Vision, Natural Language Processing, Artificial Intelligence, Python Programming

      Beginner · Course · 1 - 4 Weeks

    • Status: New
      New
      P

      Packt

      Advanced ML Algorithms & Unsupervised Learning

      Skills you'll gain: Dimensionality Reduction, Unsupervised Learning, Deep Learning, Random Forest Algorithm, Machine Learning Algorithms, Machine Learning, Decision Tree Learning, Classification And Regression Tree (CART), Feature Engineering, Supervised Learning, Statistical Machine Learning, Predictive Modeling, Artificial Intelligence, Exploratory Data Analysis

      Intermediate · Course · 1 - 3 Months

    • C

      Coursera Project Network

      Build a Machine Learning Image Classifier with Python

      Skills you'll gain: Keras (Neural Network Library), Image Analysis, Data Processing, Data Manipulation, Visualization (Computer Graphics), Applied Machine Learning, Computer Vision, Jupyter, Deep Learning, Artificial Intelligence and Machine Learning (AI/ML), Python Programming

      3.9
      Rating, 3.9 out of 5 stars
      ·
      9 reviews

      Intermediate · Guided Project · Less Than 2 Hours

    • Status: New
      New
      P

      Packt

      Natural Language Processing - Deep Learning Models in Python

      Skills you'll gain: Tensorflow, Deep Learning, Natural Language Processing, Artificial Neural Networks, Python Programming, Text Mining, Machine Learning Methods

      Intermediate · Course · 1 - 3 Months

    • U

      Universidad Austral

      NLP Modelos y Algoritmos

      Skills you'll gain: Natural Language Processing, PyTorch (Machine Learning Library), Machine Learning Algorithms, Keras (Neural Network Library), Artificial Neural Networks, Tensorflow, Deep Learning, Scikit Learn (Machine Learning Library), Python Programming, Jupyter, Machine Learning, Unsupervised Learning, Algorithms, Supervised Learning

      3.7
      Rating, 3.7 out of 5 stars
      ·
      10 reviews

      Beginner · Course · 1 - 4 Weeks

    • G

      Google Cloud

      Feature Engineering em Português Brasileiro

      Skills you'll gain: Feature Engineering, Tensorflow, MLOps (Machine Learning Operations), Data Processing, Data Transformation, Data Pipelines, Keras (Neural Network Library), Data Modeling, Machine Learning, Statistical Methods

      4.5
      Rating, 4.5 out of 5 stars
      ·
      15 reviews

      Intermediate · Course · 1 - 3 Months

    • U

      Università di Napoli Federico II

      Python per la Data Science

      Skills you'll gain: PyTorch (Machine Learning Library), NumPy, Image Analysis, Pandas (Python Package), Matplotlib, Artificial Neural Networks, Deep Learning, Computer Vision, Keras (Neural Network Library), Jupyter, Data Manipulation, Artificial Intelligence and Machine Learning (AI/ML), Applied Machine Learning, Python Programming, Data Science, Scikit Learn (Machine Learning Library)

      4.3
      Rating, 4.3 out of 5 stars
      ·
      9 reviews

      Intermediate · Course · 1 - 3 Months

    • C

      Coursera Project Network

      Deep-Dive into Tensorflow Activation Functions

      Skills you'll gain: Tensorflow, Keras (Neural Network Library), Artificial Neural Networks, Deep Learning, Applied Machine Learning, Python Programming

      4.7
      Rating, 4.7 out of 5 stars
      ·
      7 reviews

      Intermediate · Guided Project · Less Than 2 Hours

    • G

      Google Cloud

      Feature Engineering 日本語版

      Skills you'll gain: Feature Engineering, Tensorflow, Dataflow, Data Transformation, Data Processing, Keras (Neural Network Library), Data Modeling, Machine Learning, Google Cloud Platform, Statistical Methods

      4.5
      Rating, 4.5 out of 5 stars
      ·
      10 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free
      Free
      I

      Imperial College London

      TensorFlow 2 시작하기

      Skills you'll gain: Keras (Neural Network Library), Tensorflow, Deep Learning, Data Validation, Predictive Modeling, Supervised Learning, Machine Learning Methods, Artificial Neural Networks, Computer Vision, Jupyter

      Intermediate · Course · 1 - 3 Months

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    In summary, here are 10 of our most popular keras courses

    • Machine Learning with TensorFlow on Google Cloud en Français: Google Cloud
    • Medical Insurance Premium Prediction with Machine Learning: Coursera Project Network
    • Deep Learning: Del Concepto a la Práctica: Universidades Anáhuac
    • Advanced ML Algorithms & Unsupervised Learning: Packt
    • Build a Machine Learning Image Classifier with Python: Coursera Project Network
    • Natural Language Processing - Deep Learning Models in Python: Packt
    • NLP Modelos y Algoritmos: Universidad Austral
    • Feature Engineering em Português Brasileiro: Google Cloud
    • Python per la Data Science: Università di Napoli Federico II
    • Deep-Dive into Tensorflow Activation Functions: Coursera Project Network

    Frequently Asked Questions about Keras

    Since Keras is based in Python, you'll need to have experience using this programming language before starting to learn Keras. You should also have a basic understanding of foundational machine learning concepts. It's also helpful to have an understanding of what deep learning is as well as strong math skills.‎

    Learning Keras is likely right for you if you're pursuing a career in neural network framework and deep learning. Deep learning refers to methods of machine learning that are based on algorithms created in artificial neural networks that are modeled after the function and structure of the brain. You might be working toward a career or already in one as a software engineer, for example, or a data analyst, data engineer, research analyst, software developer, or bioinformatics analyst. If so, learning Keras may be right for you.‎

    TensorFlow, Theano, and CNTK are three topics you can study that are closely related to Keras because Keras runs on top of these libraries. You can also study the human brain to form a deeper understanding of the premise of neural networks. Any topic related to machine learning may also be of interest to you, such as supervised, unsupervised, and reinforcement learning; inductive, deductive, and transductive learning; or multi-task, active, online, transfer, and ensemble learning. You can also learn more about deep learning and neural networks.‎

    Places that hire people with a background in Keras include companies and organizations that employ data and software analysts and developers. For example, you'll find people with a background in Keras working for cloud computing platforms like Amazon Web Services, professional services networks like Deloitte, telecommunications companies such as Verizon, and cybersecurity companies like Carbon Black. Other notable employers of people with a background in Keras include JP Morgan Chase Bank, Microsoft, Facebook, Ford Motor Company, and Lockheed Martin.‎

    Keras is an API for machine learning applications written in Python and built on top of the open-source TensorFlow platform. It provides an efficient and easy-to-use interface for TensorFlow, which has become one of the most popular software platforms for machine learning due to its flexibility and a comprehensive ecosystem of tools and resources; for instance, the TensorFlow.js library allows you to build machine learning applications to run in web browsers on JavaScript. By allowing researchers and developers to go from their ideas to results as quickly as possible while still harnessing the power of TensorFlow, Keras is an important tool for enabling fast experimentation for machine learning applications.

    In addition to providing an approachable interface for TensorFlow, developing applications in Keras offers a number of other advantages. It allows for computation to be scaled to use many devices, harnessing potentially tens of thousands of CPUs and GPUs for advanced applications. It can also export programs to external runtimes such as servers, web browsers, or mobile and embedded devices. This flexibility and power, in addition to ease of use, has made Keras an essential tool for simple machine learning tasks as well as high-level deep learning tasks such as creating artificial neural networks.‎

    TensorFlow and Keras are essential, industry-standard tools for developing machine learning, deep learning, and artificial intelligence (AI) applications. These cutting-edge skills are in high demand from technology companies seeking to harness user data to provide new or improved services, as well as companies building next-generation products in the automotive industry, medicine, robotics, and other areas. This high demand translates into high pay; in addition to experiencing the excitement of working on the forefront of technology, AI engineers receive an average annual salary of $114,121 according to Glassdoor.‎

    Yes! In fact, Coursera lets you learn about Keras, TensorFlow, and other topics in machine learning and artificial intelligence (AI) in several different ways. You can take courses from top-ranked schools like Imperial College London, or from industry leaders like IBM and deeplearning.ai. Additionally, you can build skills in Keras by completing guided tutorials side-by-side experienced instructors with the Coursera Project Network, providing a more hands-on way to learn. Regardless of what best suits your needs, Coursera lets you learn remotely on a flexible schedule, allowing you to fit this valuable education into your existing studies, work, or family life.‎

    Online Keras courses offer a convenient and flexible way to enhance your knowledge or learn new Keras skills. Choose from a wide range of Keras courses offered by top universities and industry leaders tailored to various skill levels.‎

    When looking to enhance your workforce's skills in Keras, it's crucial to select a course that aligns with their current abilities and learning objectives. Our Skills Dashboard is an invaluable tool for identifying skill gaps and choosing the most appropriate course for effective upskilling. For a comprehensive understanding of how our courses can benefit your employees, explore the enterprise solutions we offer. Discover more about our tailored programs at Coursera for Business here.‎

    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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