Akash Kumar. E
III ECE 1
Machine learning is the process of training a machine to perform specific tasks or exhibit intelligence for desired outcomes. Before going into the basic concepts of machine learning we have to understand the origin and evolution of machine learning.
Machine Learning(ML)
Machine learning is a subset of AI that enables models to learn from data and improve their decision-making abilities over time without explicit programming. A large number of data is used to train the model to make it fit for making the right decision or prediction for the task. Machine learning is a key advancement in AI and continues to evolve beyond that. This evolution has already progressed to the next advancement known as “Deep Learning.” To learn machine learning, it’s essential to understand the learning methods used to train ML models. There are three main training methods commonly used to train models
- Supervised learning
- Unsupervised learning
- Reinforcement learning
Let’s try and understand them better.
Supervised Learning
Supervised learning is one of the simplest methods for training models, using labeled datasets. It involves training the model with both input and output data. The input data is provided to the model according to its task, and the model analyzes this data, mapping it to the corresponding output labels. For instance, to train a model to predict fruit names, you provide characteristics of the fruit as input data and the fruit’s name as the label. The model learns to associate these characteristics with the label and can then predict the fruit name based on new input data.
Unsupervised Learning
Unsupervised learning involves training a model using only input data, without any labels. The model learns patterns and structures from the data itself. For example, if we want to train a model to classify fruits, we would provide input data such as images of fruits. The model then analyzes and groups these images based on characteristics like structure, color, and shape, without any predefined labels.
Reinforcement learning
Reinforcement learning is a method where a model learns by taking actions and identifying errors to make better decisions for a given task. The model explores different actions to discover potential mistakes and improve its accuracy. For instance, in a chess game, the model tries various moves to find which ones lead to better outcomes and avoid poor moves.
These are the fundamental concepts of reinforcement learning. To gain a deeper understanding, we need to explore each learning method individually. Mastery of machine learning also requires proficiency in programming languages like Python, along with knowledge of relevant libraries and other concepts that delve deeper into ML.
In machine learning (ML), various tools and frameworks are used across different stages of the process, including building, training, and deploying models. Key programming languages like Python and R are popular for their simplicity and robust libraries.
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