Machine Learning courses by Andrew Ng focus on key concepts like supervised and unsupervised learning, neural networks, and decision trees. You’ll learn how to implement algorithms, evaluate models, and apply techniques using tools like Python and TensorFlow. These courses are designed to give you a solid foundation in how machines learn from data.

DeepLearning.AI
Beginner · Specialization

DeepLearning.AI
Skills you'll gain: Supervised Learning, Applied Machine Learning, Scikit Learn (Machine Learning Library), Machine Learning, Model Training, NumPy, Predictive Modeling, Classification Algorithms, Feature Engineering, Artificial Intelligence, Model Evaluation, Python Programming, Logistic Regression, Model Optimization, Regression Analysis
★ 4.9 (33K) · Beginner · Course · 1 - 4 Weeks

DeepLearning.AI
Skills you'll gain: Agentic Workflows, Model Context Protocol, Agentic systems, LLM Application, AI Workflows, AI Orchestration, Generative AI Agents, Tool Calling, Prompt Engineering, Model Evaluation, Automation, Debugging, Python Programming, Software Design Patterns
★ 3.9 (7) · Intermediate · Course · 1 - 3 Months

Skills you'll gain: Unsupervised Learning, Supervised Learning, Model Evaluation, Regression Analysis, Scikit Learn (Machine Learning Library), Machine Learning Methods, Applied Machine Learning, Machine Learning Algorithms, Machine Learning, Dimensionality Reduction, Python Programming, Logistic Regression, Model Optimization
★ 4.7 (18K) · Intermediate · Course · 1 - 3 Months

DeepLearning.AI
Skills you'll gain: Tensorflow, Machine Learning Methods, Machine Learning, Debugging, Performance Tuning, Data Preprocessing, Python Programming, Model Evaluation, Network Architecture
★ 4.8 (147K) · Intermediate · Specialization · 3 - 6 Months

DeepLearning.AI
Skills you'll gain: Responsible AI, Artificial Intelligence and Machine Learning (AI/ML), Artificial Intelligence, AI literacy, Machine Learning, Data Science, Deep Learning, Artificial Neural Networks
★ 4.8 (53K) · Beginner · Course · 1 - 4 Weeks

Imperial College London
Skills you'll gain: Dimensionality Reduction, Linear Algebra, Regression Analysis, NumPy, Calculus, Applied Mathematics, Descriptive Statistics, Model Optimization, Statistics, Geometry, Artificial Neural Networks, Data Science, Data Manipulation, Python Programming, Data Transformation, Machine Learning, Derivatives
★ 4.6 (15K) · Beginner · Specialization · 3 - 6 Months

Skills you'll gain: Python Programming, NumPy, Data Analysis
★ 4.6 (44K) · Beginner · Course · 1 - 3 Months

DeepLearning.AI
Skills you'll gain: Unsupervised Learning, Artificial Intelligence and Machine Learning (AI/ML), Applied Machine Learning, Responsible AI, Machine Learning, Supervised Learning, Artificial Intelligence, Reinforcement Learning, Artificial Neural Networks, Deep Learning, Anomaly Detection, Model Optimization, Dimensionality Reduction
★ 4.9 (5.8K) · Beginner · Course · 1 - 4 Weeks

University of Pennsylvania
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, Statistics, Machine Learning Methods, Machine Learning, Classification Algorithms, Artificial Intelligence, Model Optimization, Algorithms, Python Programming
★ 4.5 (60) · Intermediate · Specialization · 3 - 6 Months

Skills you'll gain: Unsupervised Learning, Exploratory Data Analysis, Autoencoders, Feature Engineering, Dimensionality Reduction, Supervised Learning, 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, Scikit Learn (Machine Learning Library), Machine Learning Algorithms, Deep Learning, Data Science, Machine Learning, Python Programming
★ 4.6 (3.7K) · Intermediate · Professional Certificate · 3 - 6 Months
Start with what you want to do with machine learning. If you want to apply it to predictive analytics, choose courses focusing on regression, classification, and model evaluation techniques. If you're interested in deep learning applications, look for courses covering neural networks, convolutional networks, and recurrent networks.
Top-rated Machine Learning Andrew Ng courses offered by DeepLearning.AI on Coursera.
Earn a certificate in Machine Learning Andrew Ng from top universities and companies.
Top-rated beginner-friendly Machine Learning Andrew Ng courses with no prerequisites.
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:
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.‎