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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

    • Status: New
      New
      E

      Edureka

      Applied Machine Learning with Python

      Skills you'll gain: Unsupervised Learning, Supervised Learning, Applied Machine Learning, Machine Learning Algorithms, Data-Driven Decision-Making, Regression Analysis, Machine Learning, Scikit Learn (Machine Learning Library), Decision Tree Learning, Data Mining, Python Programming, Analytics, Statistical Modeling, Predictive Modeling, Statistical Methods, Classification And Regression Tree (CART), Statistical Analysis, Random Forest Algorithm, Feature Engineering, Dimensionality Reduction

      Intermediate · Course · 1 - 4 Weeks

    • I

      IBM

      IBM Introduction to Machine Learning

      Skills you'll gain: Exploratory Data Analysis, Feature Engineering, Unsupervised Learning, Supervised Learning, Regression Analysis, Dimensionality Reduction, Statistical Inference, Predictive Modeling, Data Processing, Data Access, Anomaly Detection, Machine Learning, Machine Learning Algorithms, Classification And Regression Tree (CART), Scikit Learn (Machine Learning Library), Statistical Analysis, Data Analysis, Statistical Modeling, Applied Machine Learning, Data Cleansing

      4.6
      Rating, 4.6 out of 5 stars
      ·
      2.9K reviews

      Intermediate · Specialization · 3 - 6 Months

    • D

      Duke University

      MLOps | Machine Learning Operations

      Skills you'll gain: MLOps (Machine Learning Operations), Pandas (Python Package), AWS SageMaker, NumPy, Microsoft Azure, Data Ethics, Application Deployment, Data Manipulation, Exploratory Data Analysis, Containerization, Data Pipelines, CI/CD, Software Testing, Data Import/Export, Amazon Web Services, Feature Engineering, Cloud Solutions, Artificial Intelligence and Machine Learning (AI/ML), Docker (Software), Rust (Programming Language)

      4.2
      Rating, 4.2 out of 5 stars
      ·
      450 reviews

      Advanced · Specialization · 3 - 6 Months

    • D

      Duke University

      Introduction to Machine Learning

      Skills you'll gain: PyTorch (Machine Learning Library), Reinforcement Learning, Image Analysis, Applied Machine Learning, Deep Learning, Machine Learning, Natural Language Processing, Supervised Learning, Unsupervised Learning, Artificial Neural Networks, Computer Vision, Medical Imaging

      4.7
      Rating, 4.7 out of 5 stars
      ·
      3.7K reviews

      Intermediate · Course · 1 - 3 Months

    • U

      University of Washington

      Machine Learning Foundations: A Case Study Approach

      Skills you'll gain: Applied Machine Learning, Feature Engineering, Regression Analysis, Machine Learning, Image Analysis, Artificial Intelligence and Machine Learning (AI/ML), Supervised Learning, Artificial Intelligence, Deep Learning, Classification And Regression Tree (CART), Computer Vision, Application Development, Predictive Modeling, Natural Language Processing, Text Mining, Data Mining, Information Architecture

      4.6
      Rating, 4.6 out of 5 stars
      ·
      14K reviews

      Mixed · Course · 1 - 3 Months

    • I

      IBM

      Machine Learning Introduction for Everyone

      Skills you'll gain: Artificial Intelligence and Machine Learning (AI/ML), Machine Learning, Applied Machine Learning, Deep Learning, Supervised Learning, IBM Cloud, Predictive Modeling, Classification And Regression Tree (CART), Unsupervised Learning, Reinforcement Learning, Software Development Life Cycle

      4.5
      Rating, 4.5 out of 5 stars
      ·
      272 reviews

      Beginner · Course · 1 - 4 Weeks

    • U

      University of Michigan

      Applied Data Science with Python

      Skills you'll gain: Matplotlib, Network Analysis, Feature Engineering, Data Visualization Software, Interactive Data Visualization, Scientific Visualization, Pandas (Python Package), Applied Machine Learning, Supervised Learning, Text Mining, Visualization (Computer Graphics), Statistical Visualization, Scikit Learn (Machine Learning Library), Network Model, Jupyter, NumPy, Graph Theory, Data Manipulation, Natural Language Processing, Data Analysis

      4.5
      Rating, 4.5 out of 5 stars
      ·
      34K reviews

      Intermediate · Specialization · 3 - 6 Months

    • S

      Stanford University

      AI in Healthcare

      Skills you'll gain: Feature Engineering, Healthcare Ethics, Pharmaceuticals, Data Ethics, Clinical Research, Clinical Data Management, Health Systems, Healthcare Industry Knowledge, Medical Records, Unstructured Data, Health Care, Patient Evaluation, Health Informatics, Data Mining, Statistical Machine Learning, Applied Machine Learning, Electronic Medical Record, Artificial Intelligence and Machine Learning (AI/ML), Artificial Intelligence, Health Technology

      4.7
      Rating, 4.7 out of 5 stars
      ·
      2.1K reviews

      Beginner · Specialization · 3 - 6 Months

    • I

      IBM

      Introduction to Artificial Intelligence (AI)

      Skills you'll gain: Large Language Modeling, Artificial Intelligence, Generative AI, Data Ethics, Artificial Intelligence and Machine Learning (AI/ML), Applied Machine Learning, Deep Learning, Artificial Neural Networks, Governance, Prompt Engineering, Machine Learning, Automation, Digital Transformation, Business Transformation, Business Technologies, Ethical Standards And Conduct, Computer Vision, Emerging Technologies, Natural Language Processing

      4.7
      Rating, 4.7 out of 5 stars
      ·
      19K reviews

      Beginner · 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

    • Status: AI skills
      AI skills
      I

      IBM

      IBM Data Science

      Skills you'll gain: Dashboard, Data Visualization Software, Data Wrangling, Data Visualization, SQL, Supervised Learning, Feature Engineering, Plotly, Interactive Data Visualization, Jupyter, Data Literacy, Exploratory Data Analysis, Data Mining, Data Cleansing, Matplotlib, Data Analysis, Unsupervised Learning, Generative AI, Pandas (Python Package), Professional Networking

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      143K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • D

      DeepLearning.AI

      Mathematics for Machine Learning and Data Science

      Skills you'll gain: Descriptive Statistics, Bayesian Statistics, Statistical Hypothesis Testing, Probability & Statistics, Sampling (Statistics), Probability Distribution, Probability, Linear Algebra, Statistical Inference, Applied Mathematics, NumPy, Calculus, Dimensionality Reduction, Numerical Analysis, Mathematical Modeling, Machine Learning, Machine Learning Methods, Python Programming, Jupyter, Data Manipulation

      4.6
      Rating, 4.6 out of 5 stars
      ·
      2.7K reviews

      Intermediate · Specialization · 1 - 3 Months

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

    • Applied Machine Learning with Python: Edureka
    • IBM Introduction to Machine Learning: IBM
    • MLOps | Machine Learning Operations: Duke University
    • Introduction to Machine Learning: Duke University
    • Machine Learning Foundations: A Case Study Approach: University of Washington
    • Machine Learning Introduction for Everyone: IBM
    • Applied Data Science with Python: University of Michigan
    • AI in Healthcare: Stanford University
    • Introduction to Artificial Intelligence (AI): IBM
    • Introduction to Statistics: Stanford University

    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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