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    • Applied Machine Learning

    Applied Machine Learning Courses Online

    Explore applied machine learning for practical applications. Learn to implement models using scikit-learn, TensorFlow, and real-world datasets.

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    Explore the Applied Machine Learning Course Catalog

    • I

      IBM

      IBM AI Developer

      Skills you'll gain: Generative AI, Prompt Engineering, Large Language Modeling, Software Development Life Cycle, Unit Testing, ChatGPT, HTML and CSS, Development Environment, Web Design and Development, Jupyter, Hypertext Markup Language (HTML), Software Engineering, Artificial Intelligence, Cascading Style Sheets (CSS), Software Development Tools, Web Development, Application Deployment, Automation, Interviewing Skills, Professional Networking

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      68K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • J

      Johns Hopkins University

      Applied Machine Learning: Techniques and Applications

      Skills you'll gain: Computer Vision, Machine Learning Algorithms, Image Analysis, Supervised Learning, Applied Machine Learning, Feature Engineering, Data Cleansing, Scikit Learn (Machine Learning Library), Data Transformation, Predictive Modeling, Machine Learning, Data Analysis

      4.2
      Rating, 4.2 out of 5 stars
      ·
      6 reviews

      Intermediate · Course · 1 - 3 Months

    • D

      Duke University

      Machine Learning Foundations for Product Managers

      Skills you'll gain: Deep Learning, Unsupervised Learning, Classification And Regression Tree (CART), Machine Learning, Regression Analysis, Artificial Intelligence and Machine Learning (AI/ML), Artificial Neural Networks, Decision Tree Learning, Computer Vision, Supervised Learning, Natural Language Processing, Random Forest Algorithm, Algorithms, Performance Metric

      4.6
      Rating, 4.6 out of 5 stars
      ·
      548 reviews

      Beginner · Course · 1 - 3 Months

    • I

      IBM

      IBM DevOps and Software Engineering

      Skills you'll gain: User Story, CI/CD, Istio, Open Web Application Security Project (OWASP), Continuous Integration, Software Development Life Cycle, Linux Commands, Kubernetes, Unit Testing, Cloud Computing Architecture, Application Deployment, Test Driven Development (TDD), Gherkin (Scripting Language), Jenkins, Bash (Scripting Language), Agile Software Development, Git (Version Control System), DevOps, Jupyter, Grafana

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      59K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • I

      IBM

      Data Engineering Foundations

      Skills you'll gain: SQL, Web Scraping, Database Design, MySQL, Data Transformation, Data Store, IBM DB2, Extract, Transform, Load, Relational Databases, Data Architecture, Jupyter, Data Pipelines, Big Data, Data Warehousing, Data Governance, Databases, Stored Procedure, Data Manipulation, Automation, Python Programming

      4.6
      Rating, 4.6 out of 5 stars
      ·
      56K reviews

      Beginner · Specialization · 3 - 6 Months

    • I

      Imperial College London

      Mathematics for Machine Learning: Multivariate Calculus

      Skills you'll gain: Regression Analysis, Calculus, Advanced Mathematics, Machine Learning Algorithms, Statistical Analysis, Linear Algebra, Artificial Neural Networks, Python Programming, Derivatives

      4.7
      Rating, 4.7 out of 5 stars
      ·
      5.7K reviews

      Beginner · Course · 1 - 3 Months

    • D

      DeepLearning.AI

      Linear Algebra for Machine Learning and Data Science

      Skills you'll gain: Linear Algebra, NumPy, Dimensionality Reduction, Machine Learning Methods, Jupyter, Data Manipulation, Data Science, Applied Mathematics, Python Programming, Artificial Intelligence

      4.6
      Rating, 4.6 out of 5 stars
      ·
      2K reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free
      Free
      S

      Stanford University

      Introduction to Statistics

      Skills you'll gain: Descriptive Statistics, Statistics, Statistical Methods, Sampling (Statistics), Statistical Analysis, Data Analysis, Statistical Modeling, Statistical Hypothesis Testing, Regression Analysis, Statistical Inference, Probability, Exploratory Data Analysis, Quantitative Research, Data Collection, Probability Distribution

      4.6
      Rating, 4.6 out of 5 stars
      ·
      4K reviews

      Beginner · Course · 1 - 3 Months

    • U

      University of London

      Machine Learning for All

      Skills you'll gain: Feature Engineering, Machine Learning, Data Processing, Artificial Intelligence, Supervised Learning, Data Analysis, Computer Vision, Data Collection

      4.7
      Rating, 4.7 out of 5 stars
      ·
      3.5K reviews

      Beginner · Course · 1 - 4 Weeks

    • Status: Free
      Free
      C

      Coursera Project Network

      Machine Learning Pipelines with Azure ML Studio

      Skills you'll gain: Applied Machine Learning, Classification And Regression Tree (CART), Predictive Modeling, Microsoft Azure, Machine Learning, Supervised Learning, Feature Engineering, Data Pipelines, Data Processing, Data Cleansing, Application Deployment

      4.5
      Rating, 4.5 out of 5 stars
      ·
      801 reviews

      Beginner · Guided Project · Less Than 2 Hours

    • D

      DeepLearning.AI

      Calculus for Machine Learning and Data Science

      Skills you'll gain: Applied Mathematics, Calculus, Mathematical Modeling, NumPy, Machine Learning, Python Programming, Artificial Neural Networks, Visualization (Computer Graphics), Deep Learning, Derivatives

      4.8
      Rating, 4.8 out of 5 stars
      ·
      840 reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: AI skills
      AI skills
      U

      University of Pennsylvania

      AI and Machine Learning Essentials with Python

      Skills you'll gain: Statistical Machine Learning, PyTorch (Machine Learning Library), Probability, Probability & Statistics, Sampling (Statistics), Deep Learning, Probability Distribution, Python Programming, Supervised Learning, Statistics, Machine Learning, Regression Analysis, Data Processing, Agentic systems, Data Science, Artificial Intelligence, Artificial Neural Networks, Algorithms, Computer Vision, Theoretical Computer Science

      4.6
      Rating, 4.6 out of 5 stars
      ·
      19 reviews

      Intermediate · Specialization · 3 - 6 Months

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    In summary, here are 10 of our most popular applied machine learning courses

    • IBM AI Developer: IBM
    • Applied Machine Learning: Techniques and Applications: Johns Hopkins University
    • Machine Learning Foundations for Product Managers: Duke University
    • IBM DevOps and Software Engineering: IBM
    • Data Engineering Foundations: IBM
    • Mathematics for Machine Learning: Multivariate Calculus: Imperial College London
    • Linear Algebra for Machine Learning and Data Science: DeepLearning.AI
    • Introduction to Statistics: Stanford University
    • Machine Learning for All: University of London
    • Machine Learning Pipelines with Azure ML Studio: Coursera Project Network

    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 Applied Machine Learning

    It's important to learn about applied machine learning if you want to pursue a career in it or if you want to know how to use it in data analysis. Applied machine learning can be used to solve problems and collect insights from big data sets. When you learn machine learning fundamentals, you will be able to do predictive analysis, data analysis, and text mining. You'll be able to apply different programming languages and platforms to the data problems faced in your organization. Applied machine learning can help you pull insights and make decisions from the information that has been collecting for a long time.‎

    Career opportunities that arise from learning applied machine learning are mostly in computer programming. Applied machine learning, also known as automatic machine learning or AutoML, involves using computer languages for data analysis of large data. Being able to create the programs to do this is important. It is also useful to understand the power and limitations of applied machine learning if you will be using reports and making decisions based on applied machine learning. More and more management information systems include aspects of applied machine learning. This makes the field more useful to those who use data in their daily work.‎

    Online courses on Coursera can help you learn applied machine learning in several languages and platforms, including Python, Matlab, Google Cloud, H20 in R, and TensorFlow. Some courses cover programming languages, and others look at how machine learning is applied to decision making in specific fields. Most courses are at an intermediate level, but a few offer a beginner-level introduction to the fundamentals of applied machine learning and how to chart, plot, and mine text to make big data usable. The courses include lectures, readings, and projects so that you can apply what you learn. Some courses stand alone, and others are part of Specializations and Professional Certificates.‎

    Online Applied Machine Learning courses offer a convenient and flexible way to enhance your existing knowledge or learn new Applied Machine Learning skills. With a wide range of Applied Machine Learning classes, you can conveniently learn at your own pace to advance your Applied Machine Learning career skills.‎

    When looking to enhance your workforce's skills in Applied Machine Learning, 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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