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Results for "machine learning"


  • I

    IBM

    Machine Learning with Python

    Skills you'll gain: Unsupervised Learning, Supervised Learning, Model Evaluation, Regression Analysis, Scikit Learn (Machine Learning Library), Machine Learning Methods, Applied Machine Learning, Model Training, Predictive Modeling, Machine Learning Algorithms, Statistical Methods, Machine Learning, Dimensionality Reduction, Python Programming, Logistic Regression, Model Optimization, Classification Algorithms

    Coursera Plus

    Included with Coursera Plus

    4.7
    Rating, 4.7 out of 5 stars
    ·
    18K reviews

    Intermediate · Course · 1 - 3 Months

  • D

    DeepLearning.AI

    Supervised Machine Learning: Regression and Classification

    Skills you'll gain: Supervised Learning, Applied Machine Learning, Jupyter, Scikit Learn (Machine Learning Library), Machine Learning, Model Training, NumPy, Machine Learning Algorithms, Predictive Modeling, Classification Algorithms, Feature Engineering, Artificial Intelligence, Model Evaluation, Data Preprocessing, Python Programming, Logistic Regression, Model Optimization, Regression Analysis, Algorithms

    4.9
    Rating, 4.9 out of 5 stars
    ·
    33K reviews

    Beginner · Course · 1 - 4 Weeks

  • I

    IBM

    IBM Introduction to Machine Learning

    Skills you'll gain: Unsupervised Learning, Exploratory Data Analysis, Feature Engineering, Dimensionality Reduction, Supervised Learning, Classification Algorithms, Regression Analysis, Scikit Learn (Machine Learning Library), Machine Learning Algorithms, Statistical Methods, Data Preprocessing, Applied Machine Learning, Model Evaluation, Statistical Inference, Predictive Modeling, Machine Learning Methods, Statistical Hypothesis Testing, Model Training, Data Processing, Machine Learning

    Coursera Plus

    Included with Coursera Plus

    4.6
    Rating, 4.6 out of 5 stars
    ·
    3.4K reviews

    Intermediate · Specialization · 3 - 6 Months

  • A

    Amazon Web Services

    Fundamentals of Machine Learning and Artificial Intelligence

    Skills you'll gain: Artificial Intelligence and Machine Learning (AI/ML), Generative AI, Deep Learning, Artificial Intelligence, Amazon Web Services, Applied Machine Learning, AI literacy, Machine Learning, Digital Transformation

    4.6
    Rating, 4.6 out of 5 stars
    ·
    3.6K reviews

    Mixed · Course · 1 - 4 Weeks

  • D

    Duke University

    Introduction to Machine Learning

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

    Coursera Plus

    Included with Coursera Plus

    4.7
    Rating, 4.7 out of 5 stars
    ·
    3.8K reviews

    Intermediate · Course · 1 - 3 Months

  • D

    Duke University

    MLOps | Machine Learning Operations

    Skills you'll gain: Fine-tuning, MLOps (Machine Learning Operations), Model Deployment, Cloud Deployment, Pandas (Python Package), AWS SageMaker, NumPy, Microsoft Azure, Hugging Face, GitHub Copilot, Unit Testing, Responsible AI, DevOps, Cloud Computing, Python Programming, Machine Learning, GitHub, Big Data, Data Management, Data Analysis

    Coursera Plus

    Included with Coursera Plus

    4.2
    Rating, 4.2 out of 5 stars
    ·
    622 reviews

    Advanced · Specialization · 3 - 6 Months

  • C

    Coursera

    Machine Learning with Scikit-learn, PyTorch & Hugging Face

    Skills you'll gain: Generative AI, Model Evaluation, Supervised Learning, Generative Model Architectures, Recurrent Neural Networks (RNNs), Unsupervised Learning, Data Preprocessing, Large Language Modeling, Time Series Analysis and Forecasting, Exploratory Data Analysis, LLM Application, Applied Machine Learning, Data Collection, Model Optimization, Convolutional Neural Networks, Model Deployment, Transfer Learning, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning Methods, Model Training

    Coursera Plus

    Included with Coursera Plus

    4.4
    Rating, 4.4 out of 5 stars
    ·
    30 reviews

    Intermediate · Professional Certificate · 3 - 6 Months

  • D

    DeepLearning.AI

    AI For Everyone

    Skills you'll gain: AI Product Strategy, Responsible AI, Data Ethics, AI Enablement, Applied Machine Learning, Artificial Intelligence, AI literacy, Machine Learning, Data Science, AI Integrations, Deep Learning, Artificial Neural Networks

    4.8
    Rating, 4.8 out of 5 stars
    ·
    53K reviews

    Beginner · Course · 1 - 4 Weeks

  • A

    Amazon Web Services

    Developing Machine Learning Solutions

    Skills you'll gain: Model Evaluation, MLOps (Machine Learning Operations), Model Training, Amazon Web Services, AI Workflows, Model Deployment, Machine Learning Methods, Machine Learning, Applied Machine Learning

    4.5
    Rating, 4.5 out of 5 stars
    ·
    127 reviews

    Beginner · Course · 1 - 4 Weeks

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  • I

    IBM

    Exploratory Data Analysis for Machine Learning

    Skills you'll gain: Exploratory Data Analysis, Feature Engineering, Statistical Methods, Data Preprocessing, Statistical Inference, Statistical Hypothesis Testing, Data Processing, Applied Machine Learning, Data Access, Statistics, Statistical Analysis, Data Analysis, Data Cleansing, Data Manipulation, Data Science, Data Wrangling, Machine Learning, Probability & Statistics, Data Import/Export, Data Transformation

    Coursera Plus

    Included with Coursera Plus

    4.6
    Rating, 4.6 out of 5 stars
    ·
    2.6K reviews

    Intermediate · Course · 1 - 3 Months

  • 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

    Coursera Plus

    Included with Coursera Plus

    Build toward a degree

    Intermediate · Course · 1 - 3 Months

  • 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

    Coursera Plus

    Included with Coursera Plus

    4.5
    Rating, 4.5 out of 5 stars
    ·
    274 reviews

    Intermediate · Course · 1 - 3 Months

1234…816

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

  • Machine Learning with Python: IBM
  • Supervised Machine Learning: Regression and Classification : DeepLearning.AI
  • IBM Introduction to Machine Learning: IBM
  • Fundamentals of Machine Learning and Artificial Intelligence: Amazon Web Services
  • Introduction to Machine Learning: Duke University
  • MLOps | Machine Learning Operations: Duke University
  • Machine Learning with Scikit-learn, PyTorch & Hugging Face: Coursera
  • AI For Everyone: DeepLearning.AI
  • Developing Machine Learning Solutions: Amazon Web Services
  • Exploratory Data Analysis for Machine Learning: IBM

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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