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Machine Learning Courses

Machine learning courses can help you learn data preprocessing, supervised and unsupervised learning, and model evaluation techniques. You can build skills in feature engineering, algorithm selection, and hyperparameter tuning. Many courses introduce tools like Python, TensorFlow, and Scikit-learn, demonstrating how these skills are applied to create predictive models and analyze large datasets.

Popular Machine Learning Courses and Certifications


  • Status: Free Trial
    Free Trial
    D
    S

    Multiple educators

    Machine Learning

    Skills you'll gain: Unsupervised Learning, Supervised Learning, Model Training, Applied Machine Learning, Machine Learning Algorithms, Transfer Learning, Machine Learning, Jupyter, Data Ethics, Decision Tree Learning, Model Evaluation, Responsible AI, Tensorflow, Scikit Learn (Machine Learning Library), NumPy, Predictive Modeling, Deep Learning, Artificial Intelligence, Classification Algorithms, Reinforcement Learning

    4.9
    Rating, 4.9 out of 5 stars
    ·
    39K reviews

    Beginner · Specialization · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    I

    IBM

    IBM Machine Learning

    Skills you'll gain: Unsupervised Learning, Exploratory Data Analysis, Autoencoders, Feature Engineering, Dimensionality Reduction, Supervised Learning, Generative AI, Classification Algorithms, Regression Analysis, Time Series Analysis and Forecasting, Recurrent Neural Networks (RNNs), Convolutional Neural Networks, Reinforcement Learning, Generative Adversarial Networks (GANs), Generative Model Architectures, Artificial Intelligence and Machine Learning (AI/ML), Deep Learning, Data Science, Machine Learning, Python Programming

    Build toward a degree

    4.6
    Rating, 4.6 out of 5 stars
    ·
    3.7K reviews

    Intermediate · Professional Certificate · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    U

    University of Washington

    Machine Learning

    Skills you'll gain: Model Evaluation, Classification Algorithms, Regression Analysis, Applied Machine Learning, Machine Learning Methods, Feature Engineering, Machine Learning, Image Analysis, Machine Learning Algorithms, AI Personalization, Unsupervised Learning, Artificial Intelligence and Machine Learning (AI/ML), Predictive Modeling, Classification And Regression Tree (CART), Supervised Learning, Bayesian Statistics, Statistical Machine Learning, Model Training, Logistic Regression, Data Mining

    4.6
    Rating, 4.6 out of 5 stars
    ·
    16K reviews

    Intermediate · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    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), Statistical Methods, Probability Distribution, Linear Algebra, Statistical Inference, Model Optimization, Machine Learning Methods, Statistics, Applied Mathematics, Probability, Calculus, Dimensionality Reduction, Applied Machine Learning, Mathematical Software, Data Transformation, Machine Learning

    4.6
    Rating, 4.6 out of 5 stars
    ·
    3.2K reviews

    Intermediate · Specialization · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    U

    University of Michigan

    Applied Machine Learning in Python

    Skills you'll gain: Feature Engineering, Model Evaluation, Applied Machine Learning, Supervised Learning, Scikit Learn (Machine Learning Library), Predictive Modeling, Machine Learning Methods, Machine Learning, Model Training, Model Optimization, Machine Learning Algorithms, Unsupervised Learning, Python Programming, Classification Algorithms, Artificial Neural Networks

    4.6
    Rating, 4.6 out of 5 stars
    ·
    8.8K reviews

    Intermediate · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    Status: AI skills
    AI skills
    U

    University of Pennsylvania

    AI and Machine Learning Essentials with Python

    Skills you'll gain: Statistical Machine Learning, Data Preprocessing, Model Evaluation, PyTorch (Machine Learning Library), Statistical Methods, Probability, Probability & Statistics, Sampling (Statistics), Logistic Regression, Deep Learning, Probability Distribution, Statistical Modeling, Python Programming, Supervised Learning, Machine Learning, Agentic systems, Artificial Intelligence, Model Optimization, Algorithms, AI literacy

    4.5
    Rating, 4.5 out of 5 stars
    ·
    52 reviews

    Intermediate · Specialization · 3 - 6 Months

What brings you to Coursera today?

  • D

    DeepLearning.AI

    Machine Learning in Production

    Skills you'll gain: Model Deployment, MLOps (Machine Learning Operations), Application Deployment, Model Training, Continuous Deployment, Model Evaluation, Data Preprocessing, Model Optimization, Machine Learning, Applied Machine Learning, Data Validation, Data Integrity, Data Maintenance, Data Quality, Data Synthesis, Data Collection, System Monitoring, Continuous Monitoring, Unstructured Data

    4.8
    Rating, 4.8 out of 5 stars
    ·
    3.4K reviews

    Intermediate · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    D

    DeepLearning.AI

    Linear Algebra for Machine Learning and Data Science

    Skills you'll gain: Linear Algebra, Dimensionality Reduction, Mathematical Software, Machine Learning Methods, Data Transformation, Data Manipulation, Applied Mathematics, Machine Learning, Python Programming, Algebra, Image Analysis

    4.6
    Rating, 4.6 out of 5 stars
    ·
    2.4K reviews

    Intermediate · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    D

    Dartmouth College

    Machine Learning Fundamentals

    Skills you'll gain: Supervised Learning, Predictive Modeling, Logistic Regression, Statistical Modeling, Model Evaluation, Statistical Machine Learning, Machine Learning Methods, Applied Machine Learning, Machine Learning, Generative Model Architectures, Machine Learning Algorithms, Classification Algorithms, Model Optimization, Regression Analysis, Probability & Statistics

    Build toward a degree

    Intermediate · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    M

    Microsoft

    Foundations of AI and Machine Learning

    Skills you'll gain: Model Deployment, Data Management, Artificial Intelligence and Machine Learning (AI/ML), Infrastructure Architecture, Data Infrastructure, AI Integrations, MLOps (Machine Learning Operations), Application Deployment, AI Workflows, Model Evaluation, Data Cleansing, Artificial Intelligence, Data Security, Application Frameworks, Machine Learning, Data Preprocessing, Data Pipelines, Scalability

    4.5
    Rating, 4.5 out of 5 stars
    ·
    274 reviews

    Intermediate · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    G

    Google Cloud

    Machine Learning on Google Cloud

    Skills you'll gain: Feature Engineering, Model Optimization, Generative AI Agents, Model Deployment, Tensorflow, Google Cloud Platform, Model Training, Keras (Neural Network Library), Machine Learning, Data Preprocessing, Prompt Engineering, Machine Learning Software, Machine Learning Methods, MLOps (Machine Learning Operations), Generative AI, Model Evaluation, Cloud Infrastructure, Prompt Engineering Tools, Data Cleansing, Cloud Computing

    4.4
    Rating, 4.4 out of 5 stars
    ·
    3.8K reviews

    Intermediate · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    N
    G
    G
    N

    Multiple educators

    Machine Learning for Trading

    Skills you'll gain: Tensorflow, Keras (Neural Network Library), Machine Learning Methods, Model Evaluation, Machine Learning, Google Cloud Platform, Model Training, Machine Learning Algorithms, Financial Trading, Reinforcement Learning, Recurrent Neural Networks (RNNs), Supervised Learning, Data Pipelines, Machine Learning Software, Time Series Analysis and Forecasting, Applied Machine Learning, Statistical Machine Learning, Technical Analysis, Deep Learning, Portfolio Management

    3.8
    Rating, 3.8 out of 5 stars
    ·
    1.2K reviews

    Intermediate · Specialization · 1 - 3 Months

1234…86

In summary, here are 10 of our most popular Machine Learning courses

  • Machine Learning: DeepLearning.AI
  • IBM Machine Learning: IBM
  • Machine Learning: University of Washington
  • Mathematics for Machine Learning and Data Science: DeepLearning.AI
  • Applied Machine Learning in Python: University of Michigan
  • AI and Machine Learning Essentials with Python: University of Pennsylvania
  • Machine Learning in Production: DeepLearning.AI
  • Linear Algebra for Machine Learning and Data Science: DeepLearning.AI
  • Machine Learning Fundamentals: Dartmouth College
  • Foundations of AI and Machine Learning: Microsoft

Frequently Asked Questions about Machine Learning

Machine learning is a subset of artificial intelligence that enables systems to learn from data, identify patterns, and make decisions with minimal human intervention. It is important because it drives innovation across various sectors, from healthcare to finance, by automating processes and providing insights that were previously unattainable. As industries increasingly rely on data-driven decision-making, understanding machine learning becomes essential for staying competitive.‎

A variety of job opportunities exist in the field of machine learning. Positions include machine learning engineer, data scientist, AI researcher, and business intelligence analyst. These roles often require a blend of programming skills, statistical knowledge, and domain expertise. As organizations continue to adopt machine learning technologies, the demand for skilled professionals in this area is expected to grow.‎

To learn machine learning effectively, you should focus on several key skills. Proficiency in programming languages such as Python or R is crucial, along with a solid understanding of statistics and linear algebra. Familiarity with data manipulation and visualization tools, as well as experience with machine learning frameworks like TensorFlow or PyTorch, will also be beneficial. These skills will provide a strong foundation for your machine learning journey.‎

There are many excellent online resources for learning machine learning. Notable options include the IBM Machine Learning Professional Certificate and the Machine Learning with Scikit-learn, PyTorch & Hugging Face Professional Certificate. These programs offer structured learning paths and hands-on projects to help you build practical skills.‎

Yes. You can start learning Machine Learning on Coursera for free in two ways:

  1. Preview the first module of many Machine Learning courses at no cost. This includes video lessons, readings, graded assignments, and Coursera Coach (where available).
  2. Start a 7-day free trial for Specializations or Coursera Plus. This gives you full access to all course content across eligible programs within the timeframe of your trial.

If you want to keep learning, earn a certificate in Machine Learning, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎

To learn machine learning, start by taking introductory courses that cover the basics of algorithms and data analysis. Engage in hands-on projects to apply what you've learned, and gradually progress to more advanced topics. Utilize online resources, participate in forums, and collaborate with peers to enhance your understanding. Consistent practice and real-world application will reinforce your skills.‎

Typical topics covered in machine learning courses include supervised and unsupervised learning, regression analysis, classification techniques, clustering, and neural networks. Additionally, courses often explore data preprocessing, feature engineering, and model evaluation. Understanding these concepts will equip you with the knowledge needed to tackle various machine learning challenges.‎

For training and upskilling employees in machine learning, programs like the Applied Machine Learning Specialization are highly effective. These courses focus on practical applications and real-world scenarios, making them suitable for professionals looking to enhance their skills and contribute to their organizations' data-driven initiatives.‎

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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