Explore applied machine learning for practical applications. Learn to implement models using scikit-learn, TensorFlow, and real-world datasets.
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
Intermediate · Course · 1 - 4 Weeks

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
Intermediate · Specialization · 3 - 6 Months

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, Decision Tree Learning, Data Ethics, Tensorflow, Responsible AI, Scikit Learn (Machine Learning Library), NumPy, Predictive Modeling, Deep Learning, Artificial Intelligence, Reinforcement Learning, Random Forest Algorithm, Feature Engineering, Python Programming
Beginner · Specialization · 1 - 3 Months

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)
Beginner · Specialization · 3 - 6 Months

DeepLearning.AI
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
Beginner · Course · 1 - 4 Weeks

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
Intermediate · Course · 1 - 4 Weeks

Coursera
Skills you'll gain: Data Processing
Intermediate · Course · 1 - 3 Months

Duke University
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
Intermediate · Course · 1 - 3 Months
University of London
Skills you'll gain: Machine Learning, Data Processing, Artificial Intelligence, Data Analysis, Machine Learning Algorithms, Computer Vision, Data Collection, Software Testing
Build toward a degree
Beginner · Course · 1 - 4 Weeks

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
Intermediate · Specialization · 1 - 3 Months

Skills you'll gain: Regression Analysis, Matplotlib, Feature Engineering, Time Series Analysis and Forecasting, Jupyter, Image Analysis, Scikit Learn (Machine Learning Library), Applied Machine Learning, Tensorflow, Data Visualization, Machine Learning Algorithms, Amazon Web Services, Python Programming, Cloud Applications, Data Transformation, Predictive Modeling, Data Processing, Health Informatics, Machine Learning, Artificial Intelligence and Machine Learning (AI/ML)
Beginner · Specialization · 1 - 3 Months

DeepLearning.AI
Skills you'll gain: Descriptive Statistics, Bayesian Statistics, Statistical Hypothesis Testing, Probability & Statistics, Sampling (Statistics), Probability Distribution, Linear Algebra, Statistical Inference, A/B Testing, Statistical Analysis, Applied Mathematics, NumPy, Probability, Calculus, Dimensionality Reduction, Numerical Analysis, Mathematical Modeling, Machine Learning, Machine Learning Methods, Data Transformation
Intermediate · Specialization · 1 - 3 Months
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.‎