Applied Machine Learning

Applied Machine Learning is a multidisciplinary approach to constructing algorithms that can learn from and predict future data. Coursera's Applied Machine Learning catalogue provides you with the necessary knowledge and skills to effectively use machine learning in a range of practical applications. You'll learn how to process and analyze large-scale data, build predictive models using supervised and unsupervised learning techniques, and apply these models to real-world problems such as image and speech recognition, autonomous driving, and predictive analytics. Enhance your problem-solving abilities and gain a competitive edge in fields like data science, artificial intelligence, and software engineering by mastering machine learning techniques such as decision trees, neural networks, regression, and clustering.
123credentials
1online degree
437courses

Explore the Applied Machine Learning Course Catalog

  • Status: Free Trial

    University of Michigan

    Skills you'll gain: Feature Engineering, Applied Machine Learning, Supervised Learning, Scikit Learn (Machine Learning Library), Predictive Modeling, Machine Learning, Decision Tree Learning, Unsupervised Learning, Python Programming, Dimensionality Reduction, Random Forest Algorithm, Regression Analysis

  • Status: Free Trial

    Johns Hopkins University

    Skills you'll gain: PyTorch (Machine Learning Library), Unsupervised Learning, Computer Vision, Machine Learning Algorithms, Applied Machine Learning, Image Analysis, Dimensionality Reduction, Supervised Learning, Data Processing, Reinforcement Learning, Feature Engineering, Regression Analysis, Data Cleansing, Machine Learning, Data Mining, Scikit Learn (Machine Learning Library), Statistical Machine Learning, Deep Learning, Artificial Neural Networks, Decision Tree Learning

  • Status: New
    Status: Free Trial

    Skills you'll gain: Sampling (Statistics), Matplotlib, Data Analysis, Data Mining, Statistical Analysis, Statistical Hypothesis Testing, NumPy, Pandas (Python Package), Probability Distribution, Dimensionality Reduction, R Programming, Probability, Python Programming, Scikit Learn (Machine Learning Library), Linear Algebra, Applied Machine Learning, Unsupervised Learning, Regression Analysis, Statistical Methods, Artificial Intelligence and Machine Learning (AI/ML)

  • Status: Free Trial

    Skills you'll gain: Supervised Learning, Applied Machine Learning, Jupyter, Scikit Learn (Machine Learning Library), Machine Learning, NumPy, Predictive Modeling, Feature Engineering, Artificial Intelligence, Classification And Regression Tree (CART), Python Programming, Regression Analysis, Statistical Modeling, Data Transformation

  • Status: Free Trial

    Multiple educators

    Skills you'll gain: Unsupervised Learning, Supervised Learning, Classification And Regression Tree (CART), Artificial Intelligence and Machine Learning (AI/ML), Applied Machine Learning, Machine Learning, Jupyter, Data Ethics, Decision Tree Learning, Tensorflow, Responsible AI, Scikit Learn (Machine Learning Library), NumPy, Predictive Modeling, Deep Learning, Artificial Intelligence, Reinforcement Learning, Random Forest Algorithm, Feature Engineering, Python Programming

  • Status: Free Trial

    Skills you'll gain: Exploratory Data Analysis, Feature Engineering, Unsupervised Learning, Supervised Learning, Regression Analysis, Dimensionality Reduction, Time Series Analysis and Forecasting, Reinforcement Learning, Generative Model Architectures, Deep Learning, Data Analysis, Statistical Methods, Statistical Inference, Applied Machine Learning, Predictive Modeling, Statistical Hypothesis Testing, Machine Learning Algorithms, Machine Learning, Data Science, Python Programming

What brings you to Coursera today?

  • Status: Free Trial

    Skills you'll gain: Supervised Learning, Reinforcement Learning, Applied Machine Learning, Machine Learning, Statistical Methods, Dimensionality Reduction, Unsupervised Learning, Artificial Neural Networks, Decision Tree Learning, Predictive Modeling, Financial Trading, Financial Market, Derivatives, Scikit Learn (Machine Learning Library), Markov Model, Regression Analysis, Market Dynamics, Risk Modeling, Tensorflow, Financial Modeling

  • Status: Free Trial

    Alberta Machine Intelligence Institute

    Skills you'll gain: Data Ethics, Applied Machine Learning, Data Processing, Machine Learning, Machine Learning Algorithms, Product Lifecycle Management, Supervised Learning, Business Requirements, Data Quality, Business Analysis, Unsupervised Learning, Artificial Intelligence, Performance Metric

  • Status: Free Trial

    University of Washington

    Skills you'll gain: Regression Analysis, Applied Machine Learning, Feature Engineering, Machine Learning, Image Analysis, Unsupervised Learning, Predictive Modeling, Classification And Regression Tree (CART), Supervised Learning, Bayesian Statistics, Statistical Modeling, Artificial Intelligence, Deep Learning, Data Mining, Computer Vision, Statistical Machine Learning, Predictive Analytics, Text Mining, Machine Learning Algorithms, Big Data

  • Status: Preview

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

  • Status: New
    Status: Free Trial

    Skills you'll gain: Supervised Learning, Unsupervised Learning, Time Series Analysis and Forecasting, Applied Machine Learning, Machine Learning Algorithms, Feature Engineering, Dimensionality Reduction, Machine Learning, Predictive Modeling, Predictive Analytics, Scikit Learn (Machine Learning Library), Forecasting, Data Processing, Anomaly Detection, Data Manipulation, Regression Analysis, Statistical Modeling, Data Transformation, Data Cleansing

  • Status: New
    Status: Free Trial

    Skills you'll gain: Feature Engineering, Microsoft Azure, Applied Machine Learning, Machine Learning, Machine Learning Algorithms, Data Processing, Data Cleansing, Supervised Learning, Data Transformation, MLOps (Machine Learning Operations), Application Deployment, Artificial Intelligence and Machine Learning (AI/ML), CI/CD, Statistical Methods, Data Quality, Real Time Data, Resource Management

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