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    • Tensorflow

    TensorFlow Courses Online

    Master TensorFlow for deep learning. Learn to build, train, and deploy neural networks using TensorFlow.

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    Explore the TensorFlow Course Catalog

    • C

      Coursera Project Network

      Explainable AI: Scene Classification and GradCam Visualization

      Skills you'll gain: Keras (Neural Network Library), Deep Learning, Image Analysis, Tensorflow, Computer Vision, Applied Machine Learning, Artificial Neural Networks, Data Processing, Interactive Data Visualization

      4.6
      Rating, 4.6 out of 5 stars
      ·
      57 reviews

      Intermediate · Guided Project · Less Than 2 Hours

    • C

      Coursera Project Network

      Data Balancing with Gen AI: Credit Card Fraud Detection

      Skills you'll gain: Generative AI, Keras (Neural Network Library), Tensorflow, Deep Learning, Dimensionality Reduction, Applied Machine Learning, Anomaly Detection, Data Processing, Data Visualization, Data Synthesis

      4.3
      Rating, 4.3 out of 5 stars
      ·
      6 reviews

      Intermediate · Guided Project · Less Than 2 Hours

    • Status: Free
      Free
      U

      Universidade de São Paulo

      Processamento Neural de Linguagem Natural em Português I

      Skills you'll gain: Natural Language Processing, Artificial Neural Networks, Tensorflow, Python Programming, Artificial Intelligence and Machine Learning (AI/ML), Applied Machine Learning, Deep Learning, Text Mining

      4.6
      Rating, 4.6 out of 5 stars
      ·
      18 reviews

      Intermediate · Course · 1 - 3 Months

    • C

      Coursera Project Network

      Interpretable machine learning applications: Part 3

      Skills you'll gain: Artificial Neural Networks, Tensorflow, Keras (Neural Network Library), Data-Driven Decision-Making, Applied Machine Learning, Financial Forecasting, Business Analytics, Predictive Modeling, Deep Learning, Machine Learning, Commercial Lending, Decision Making, Mortgage Loans, Exploratory Data Analysis, Consumer Lending

      4.5
      Rating, 4.5 out of 5 stars
      ·
      20 reviews

      Intermediate · Guided Project · Less Than 2 Hours

    • G

      Google Cloud

      Smart Analytics, Machine Learning, and AI on GCP en Español

      Skills you'll gain: Google Cloud Platform, Unstructured Data, MLOps (Machine Learning Operations), Tensorflow, Big Data, Data Pipelines, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning, Natural Language Processing

      4.8
      Rating, 4.8 out of 5 stars
      ·
      44 reviews

      Intermediate · Course · 1 - 3 Months

    • G

      Google Cloud

      ML Pipelines on Google Cloud en Español

      Skills you'll gain: MLOps (Machine Learning Operations), CI/CD, Google Cloud Platform, Apache Airflow, Data Pipelines, Kubernetes, Tensorflow, Metadata Management, PyTorch (Machine Learning Library), Containerization

      Advanced · Course · 1 - 3 Months

    • G

      Google Cloud

      Build, Train and Deploy ML Models with Keras on Google Cloud - Português Brasileiro

      Skills you'll gain: Tensorflow, Keras (Neural Network Library), Google Cloud Platform, Data Pipelines, MLOps (Machine Learning Operations), Application Deployment, Deep Learning, Artificial Neural Networks, Data Cleansing, Scalability, Data Transformation, Machine Learning, Application Programming Interface (API)

      Intermediate · Course · 1 - 3 Months

    • G

      Google Cloud

      ML Pipelines on Google Cloud - 日本語版

      Skills you'll gain: Google Cloud Platform, MLOps (Machine Learning Operations), CI/CD, Tensorflow, Apache Airflow, Data Pipelines, Metadata Management, PyTorch (Machine Learning Library), Containerization, Machine Learning

      Advanced · Course · 1 - 3 Months

    • G

      Google Cloud

      ML Pipelines on Google Cloud - Français

      Skills you'll gain: Google Cloud Platform, CI/CD, Apache Airflow, MLOps (Machine Learning Operations), Tensorflow, Data Pipelines, Continuous Deployment, Metadata Management, Machine Learning Methods, PyTorch (Machine Learning Library), Containerization

      Advanced · Course · 1 - 3 Months

    • G

      Google Cloud

      Build, Train and Deploy ML Models with Keras on Google Cloud - 한국어

      Skills you'll gain: Tensorflow, Keras (Neural Network Library), Data Pipelines, Google Cloud Platform, Artificial Neural Networks, Data Transformation, Deep Learning, MLOps (Machine Learning Operations), Data Processing, Cloud Computing, Application Programming Interface (API), Application Frameworks

      Intermediate · Course · 1 - 3 Months

    • U

      University of Colorado Boulder

      Text Marketing Analytics

      Skills you'll gain: Network Analysis, Unsupervised Learning, Supervised Learning, Unstructured Data, Marketing Analytics, Machine Learning, Deep Learning, Text Mining, Machine Learning Algorithms, Tensorflow, Machine Learning Methods, Scikit Learn (Machine Learning Library), Python Programming, Data Science, Analytics, Marketing, JSON, Semantic Web, Data Structures, Natural Language Processing

      Build toward a degree

      3.1
      Rating, 3.1 out of 5 stars
      ·
      15 reviews

      Beginner · Specialization · 3 - 6 Months

    • G

      Google Cloud

      ML Pipelines on Google Cloud - Português

      Skills you'll gain: CI/CD, MLOps (Machine Learning Operations), Apache Airflow, Data Pipelines, Continuous Integration, Google Cloud Platform, Tensorflow, Kubernetes, Continuous Deployment, Metadata Management, Scikit Learn (Machine Learning Library)

      Advanced · Course · 1 - 3 Months

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

    • Explainable AI: Scene Classification and GradCam Visualization : Coursera Project Network
    • Data Balancing with Gen AI: Credit Card Fraud Detection: Coursera Project Network
    • Processamento Neural de Linguagem Natural em Português I: Universidade de São Paulo
    • Interpretable machine learning applications: Part 3: Coursera Project Network
    • Smart Analytics, Machine Learning, and AI on GCP en Español: Google Cloud
    • ML Pipelines on Google Cloud en Español: Google Cloud
    • Build, Train and Deploy ML Models with Keras on Google Cloud - Português Brasileiro: Google Cloud
    • ML Pipelines on Google Cloud - 日本語版: Google Cloud
    • ML Pipelines on Google Cloud - Français: Google Cloud
    • Build, Train and Deploy ML Models with Keras on Google Cloud - 한국어: Google Cloud

    Skills you can learn in Machine Learning

    Python Programming (33)
    Tensorflow (32)
    Deep Learning (30)
    Artificial Neural Network (24)
    Big Data (18)
    Statistical Classification (17)
    Reinforcement Learning (13)
    Algebra (10)
    Bayesian (10)
    Linear Algebra (10)
    Linear Regression (9)
    Numpy (9)

    Frequently Asked Questions about Tensorflow

    TensorFlow is an open-source framework for machine learning (ML) programming originally created by Google Brain, Google’s deep learning and artificial intelligence (AI) research team. It 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 example, TensorFlow.js allows for JavaScript-based ML applications that can run in browsers; TensorFlow Lite can run on mobile devices for federated learning applications; and TensorFlow Hub provides an extensive library of reusable ML models.

    The flexibility of TensorFlow and breadth of its machine learning applications have been important in enabling a wide range of uses. TensorFlow is frequently used for computer vision applications, including facial recognition in social media, automatic X-ray scanning in healthcare, and autonomous vehicle driving. Similarly, natural language processing (NLP) applications can understand and respond to spoken and written text, making possible the creation of helpful chatbots and other digital agents as well as the automatic reading and summarization of text. Recommendation engines used by music streaming services and online retailers may also be built in TensorFlow.

    These are all just a few examples of the power of machine learning applications and the ways that TensorFlow can be leveraged to enable them. If you’re interested in pushing the boundaries of this fast-changing field even further, learning TensorFlow is essential.‎

    Expertise in TensorFlow is an extremely valuable addition to your skillset, and can open the door to many exciting careers. As one of the most popular and useful platforms for machine learning and deep learning applications, TensorFlow skills are in demand from companies throughout the tech world, as well as in the automotive industry, medicine, robotics, and other fields. This high level of demand for skills in TensorFlow and machine learning translates into high levels of pay; according to Glassdoor, machine learning engineers in America earn an average salary of $114,121.‎

    Absolutely - in fact, Coursera is one of the best places to learn TensorFlow skills online. You can take individual courses as well as Specializations spanning multiple courses from deeplearning.ai, one of the pioneers in the field, or Google Cloud, an industry leader. You can also take courses from top-ranked universities from around the world, including Imperial College London and National Research University Higher School of Economics. Guided Projects from Coursera offer another way to learn, with hands-on Tensorflow tutorials presented by experienced instructors.‎

    You need to have a basic understanding of Python before starting to learn TensorFlow, so it's best to start with an introductory course to this programming language first. Python is the language used to design TensorFlow. It's also helpful to have knowledge of artificial intelligence (AI) concepts as well. You should have strong math skills, especially in algebra so that you'll be familiar with the calculations and algorithms required in TensorFlow. Foundational knowledge of vectors, scalars, and matrices is also very helpful as you start learning TensorFlow, as well as basic statistics. And it's important to know the basics of machine learning as well.‎

    People who are best suited for roles in TensorFlow have an interest in machine learning or deep learning. Important soft skills include communication skills, problem-solving skills, time management, teamwork, and a thirst for learning. Someone who uses TensorFlow in their job likely works with a team of professionals like software engineers, research scientists, marketing teams, data scientists, and product teams, so they must be able to communicate clearly, prioritize tasks, and work toward a common goal. And since fields that use TensorFlow—such as AI, machine learning, and deep learning—are constantly evolving, people who adapt well to change and are eager to learn or develop the next new technology are well suited for these roles.‎

    If you are currently in the machine learning field or aspire to be, learning about TensorFlow is most likely right for you. The same applies if you want to enter the deep learning field in positions like deep learning scientist, deep learning software engineer, or deep learning researcher since TensorFlow is a good starting point for deep learning. If you're in a deep learning internship, learning TensorFlow is right for you as well.‎

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

    When looking to enhance your workforce's skills in TensorFlow, 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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