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  • Machine Learning Andrew Ng

Machine Learning Andrew Ng Courses

Machine learning courses can help you learn algorithms, data preprocessing, model evaluation, and neural networks. You can build skills in regression analysis, classification techniques, and clustering methods. Many courses introduce tools like Python, TensorFlow, and Scikit-learn, showing how these technologies are used to implement machine learning solutions in real-world applications.


Popular Machine Learning Andrew Ng Courses and Certifications


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    Multiple educators

    Machine Learning

    Skills you'll gain: Unsupervised Learning, Supervised Learning, Transfer Learning, Machine Learning, Jupyter, Applied Machine Learning, Data Ethics, Decision Tree Learning, Model Evaluation, Tensorflow, Scikit Learn (Machine Learning Library), NumPy, Predictive Modeling, Deep Learning, Artificial Intelligence, Classification Algorithms, Reinforcement Learning, Random Forest Algorithm, Feature Engineering, Data Preprocessing

    4.9
    Rating, 4.9 out of 5 stars
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    38K reviews

    Beginner · Specialization · 1 - 3 Months

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    DeepLearning.AI

    Supervised Machine Learning: Regression and Classification

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

    4.9
    Rating, 4.9 out of 5 stars
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    32K reviews

    Beginner · Course · 1 - 4 Weeks

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    DeepLearning.AI

    Deep Learning

    Skills you'll gain: Convolutional Neural Networks, Recurrent Neural Networks (RNNs), Computer Vision, Transfer Learning, Deep Learning, Image Analysis, Hugging Face, Natural Language Processing, Artificial Neural Networks, Tensorflow, Embeddings, Supervised Learning, Keras (Neural Network Library), Applied Machine Learning, Machine Learning, MLOps (Machine Learning Operations), Debugging, Performance Tuning, PyTorch (Machine Learning Library), Data Preprocessing

    Build toward a degree

    4.8
    Rating, 4.8 out of 5 stars
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    147K reviews

    Intermediate · Specialization · 3 - 6 Months

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    IBM

    Machine Learning with Python

    Skills you'll gain: Unsupervised Learning, Supervised Learning, Model Evaluation, Regression Analysis, Scikit Learn (Machine Learning Library), Applied Machine Learning, Predictive Modeling, Machine Learning, Dimensionality Reduction, Decision Tree Learning, Python Programming, Logistic Regression, Classification Algorithms, Feature Engineering

    4.7
    Rating, 4.7 out of 5 stars
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    18K reviews

    Intermediate · Course · 1 - 3 Months

  • Status: Preview
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    DeepLearning.AI

    Generative AI for Everyone

    Skills you'll gain: Prompt Engineering, Responsible AI, Generative AI, Large Language Modeling, ChatGPT, AI Product Strategy, AI Security, Artificial Intelligence, Automation, Cloud Applications, Social Impact

    4.8
    Rating, 4.8 out of 5 stars
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    4.8K reviews

    Beginner · Course · 1 - 4 Weeks

  • Status: Free
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    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, AI Enablement, Artificial Intelligence, Amazon Web Services, Applied Machine Learning, Machine Learning, Digital Transformation

    4.6
    Rating, 4.6 out of 5 stars
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    2.5K reviews

    Mixed · Course · 1 - 4 Weeks

What brings you to Coursera today?

  • Status: Preview
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    DeepLearning.AI

    AI For Everyone

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

    4.8
    Rating, 4.8 out of 5 stars
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    51K reviews

    Beginner · Course · 1 - 4 Weeks

  • Status: Free Trial
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    Imperial College London

    Mathematics for Machine Learning

    Skills you'll gain: Linear Algebra, Dimensionality Reduction, NumPy, Regression Analysis, Calculus, Applied Mathematics, Data Preprocessing, Unsupervised Learning, Feature Engineering, Machine Learning Algorithms, Jupyter, Advanced Mathematics, Statistics, Artificial Neural Networks, Algorithms, Mathematical Modeling, Python Programming, Derivatives

    4.6
    Rating, 4.6 out of 5 stars
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    15K reviews

    Beginner · Specialization · 3 - 6 Months

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    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, Linear Algebra, Statistical Inference, A/B Testing, Statistical Analysis, Applied Mathematics, NumPy, Probability, Calculus, Dimensionality Reduction, Numerical Analysis, Machine Learning Algorithms, Data Preprocessing, Machine Learning, Machine Learning Methods

    4.6
    Rating, 4.6 out of 5 stars
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    3.1K reviews

    Intermediate · Specialization · 1 - 3 Months

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    DeepLearning.AI

    Advanced Learning Algorithms

    Skills you'll gain: Transfer Learning, Machine Learning, Applied Machine Learning, Data Ethics, Decision Tree Learning, Model Evaluation, Tensorflow, Artificial Intelligence, Supervised Learning, Deep Learning, Classification Algorithms, Random Forest Algorithm, Artificial Neural Networks, Logistic Regression, Performance Tuning

    4.9
    Rating, 4.9 out of 5 stars
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    8.5K reviews

    Beginner · Course · 1 - 4 Weeks

  • Status: Free Trial
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    IBM

    IBM Machine Learning

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

    Build toward a degree

    4.6
    Rating, 4.6 out of 5 stars
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    3.5K reviews

    Intermediate · Professional Certificate · 3 - 6 Months

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    DeepLearning.AI

    Machine Learning in Production

    Skills you'll gain: Model Deployment, MLOps (Machine Learning Operations), Cloud Deployment, Continuous Deployment, Model Evaluation, Data Preprocessing, Machine Learning, Applied Machine Learning, Data Validation, Feature Engineering, Data Quality, Debugging, Continuous Monitoring, Data Pipelines

    4.8
    Rating, 4.8 out of 5 stars
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    3.3K reviews

    Intermediate · Course · 1 - 4 Weeks

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

  • Machine Learning: DeepLearning.AI
  • Supervised Machine Learning: Regression and Classification : DeepLearning.AI
  • Deep Learning: DeepLearning.AI
  • Machine Learning with Python: IBM
  • Generative AI for Everyone: DeepLearning.AI
  • Fundamentals of Machine Learning and Artificial Intelligence: Amazon Web Services
  • AI For Everyone: DeepLearning.AI
  • Mathematics for Machine Learning: Imperial College London
  • Mathematics for Machine Learning and Data Science: DeepLearning.AI
  • Advanced Learning Algorithms: DeepLearning.AI

Frequently Asked Questions about Machine Learning Andrew Ng

Machine learning, particularly as taught by Andrew Ng, is a subset of artificial intelligence that focuses on the development of algorithms that allow computers to learn from and make predictions based on data. This field is crucial because it enables organizations to automate processes, enhance decision-making, and derive insights from vast amounts of information. Understanding machine learning can empower individuals to tackle complex problems across various industries, making it a valuable skill in today's data-driven world.‎

Pursuing machine learning skills can open doors to various job opportunities. Roles such as machine learning engineer, data scientist, AI researcher, and business intelligence analyst are just a few examples. These positions often involve developing algorithms, analyzing data, and implementing machine learning models to solve real-world problems. As organizations increasingly rely on data-driven insights, the demand for professionals skilled in machine learning continues to grow.‎

To succeed in machine learning, you should develop a strong foundation in several key skills. Proficiency in programming languages like Python or R is essential, as they are commonly used for building machine learning models. Additionally, understanding statistics, linear algebra, and calculus will help you grasp the underlying principles of algorithms. Familiarity with data manipulation and visualization tools, as well as experience with machine learning libraries such as TensorFlow or Scikit-learn, will further enhance your capabilities.‎

There are several excellent online courses available for learning machine learning from Andrew Ng. Notably, the IBM Machine Learning Professional Certificate offers a comprehensive introduction to the field. Additionally, the Machine Learning with Scikit-learn, PyTorch & Hugging Face Professional Certificate provides hands-on experience with popular tools and frameworks. These courses are designed to equip you with practical skills and knowledge applicable in real-world scenarios.‎

Yes. You can start learning machine learning with Andrew Ng on Coursera for free in two ways:

  1. Preview the first module of many Andrew Ng machine learning courses at no cost. This typically 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 during the trial period.

If you want to continue learning, earn a certificate in machine learning, or unlock full course access after the preview or trial, you can upgrade your enrollment or apply for financial aid.‎

To learn machine learning effectively, start by identifying your learning goals and the specific areas you want to focus on. Enroll in introductory courses, such as those offered by Andrew Ng, to build a solid foundation. Practice coding and working with datasets to reinforce your understanding. Engage with online communities or study groups to discuss concepts and share insights. Consistent practice and application of what you learn will help solidify your skills.‎

Typical topics covered in machine learning courses include supervised and unsupervised learning, regression analysis, classification algorithms, clustering techniques, and neural networks. Courses often explore practical applications of these concepts, such as natural language processing and computer vision. Additionally, you may learn about model evaluation, feature engineering, and the ethical implications of machine learning, providing a well-rounded understanding of the field.‎

For training and upskilling employees in machine learning, the AI and Machine Learning Essentials with Python Specialization is an excellent choice. This program covers fundamental concepts and practical applications, making it suitable for professionals looking to enhance their skills. Additionally, the Applied Machine Learning Specialization offers hands-on experience, which can be beneficial for teams aiming to implement machine learning solutions in their organizations.‎

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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