Скачать или смотреть Tensorflow full course in hindi || what is machine learning Basics

Tensorflow full course in hindi || what is machine learning Basics#tensorflow #machinelearning


Machine learning is a subfield of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computers to perform tasks without explicit programming. The primary goal of machine learning is to enable computers learn and improve from experience.

In traditional programming, humans write explicit instructions or rules for a computer to follow. In contrast, machine learning algorithms allow computers to learn patterns and make decisions based on data. The learning process involves the algorithm being trained on a dataset, which consists of examples or instances that represent the problem or task at hand. The algorithm learns from this data, identifying patterns and making predictions or decisions without being explicitly programmed.

There are various types of machine learning :

Supervised Learning: The algorithm is trained on a labeled dataset, where each input is paired with the corresponding correct output. The goal is to learn a mapping from inputs to outputs.

Unsupervised Learning: The algorithm is given unlabeled data and must find patterns or relationships within the data without explicit guidance. Clustering and dimensionality reduction are common task in unsupervised learning.

Reinforcement Learning: The algorithm learns by interacting with an environment and receiving feedback in the form of rewards or punishments. The goal is to learn a strategy or policy that maximizes the cumulative reward over time.

Semi-Supervised Learning: This is a combination of supervised and unsupervised learning. The algorithm is trained on a dataset that contains both labeled and unlabeled data.

Deep Learning: A subset of machine learning that involves neural networks with multiple layers (deep neural networks). Deep learning has been particularly successful in tasks such as image and speech recognition.

Machine learning is applied in a wide range of domains, including image and speech recognition, natural language processing, recommendation systems, autonomous vehicles, healthcare, finance, and many others. The field continues to evolve, with ongoing research and advancements contributing to its growth , application in various industries.


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