Machine learning (ML)
What is Machine learning (ML)?
Machine learning is a branch of artificial intelligence that involves developing algorithms and models that can automatically recognize patterns and make predictions or classifications based on input data. In other words, it's a method of training computers to learn from data, without being explicitly programmed to do so. The goal of machine learning is to create intelligent systems that can learn and improve over time, based on feedback from real-world data. Machine learning has many practical applications, including image recognition, natural language processing, fraud detection, and more.What is machine learning?
Machine learning is a branch of artificial intelligence that involves training algorithms to recognize patterns in data and make predictions or classifications based on those patterns.
What are some common types of machine learning algorithms?
Some common types of machine learning algorithms include supervised learning, unsupervised learning, and reinforcement learning.
What kind of data is required for machine learning?
Machine learning algorithms require large amounts of labeled data in order to train effectively. The quality and quantity of the data can have a significant impact on the performance of the model.
What are some popular machine learning frameworks and libraries?
Some popular machine learning frameworks and libraries include TensorFlow, PyTorch, Scikit-learn, and Keras.
What are some applications of machine learning?
Machine learning has been applied to a wide range of fields, including computer vision, natural language processing, speech recognition, recommendation systems, and more.
What is the difference between supervised and unsupervised learning?
Supervised learning involves training a model on labeled data, where the input data and output labels are known. Unsupervised learning involves training a model on unlabeled data, where the model must find patterns and structure in the data without any guidance.
Is machine learning difficult to learn?
While machine learning can be challenging to learn for beginners, there are many resources available, including online courses, books, and tutorials. With dedication and practice, most developers should be able to learn the fundamentals of machine learning and start building their own models.
- Snippet from Wikipedia: Machine learning
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can effectively generalize and thus perform tasks without explicit instructions. Recently, generative artificial neural networks have been able to surpass many previous approaches in performance. Machine learning approaches have been applied to large language models, computer vision, speech recognition, email filtering, agriculture, and medicine, where it is too costly to develop algorithms to perform the needed tasks.
The mathematical foundations of ML are provided by mathematical optimization (mathematical programming) methods. Data mining is a related (parallel) field of study, focusing on exploratory data analysis through unsupervised learning.
ML is known in its application across business problems under the name predictive analytics. Although not all machine learning is statistically based, computational statistics is an important source of the field's methods.
GitHub Collections
Today, machine learning—the study of algorithms that make data-based predictions—has found a new audience and a new set of possibilities.
Related:
External links:
- What Is Machine Learning And How Does It Work? — nvidia.com
- What Is Machine Learning? | IBM — ibm.com
- Machine Learning Crash Course — developers.google.com
- Machine learning, explained | MIT Sloan — mitsloan.mit.edu
- machine teaching
