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Keywords: Machine Learning, Artificial Intelligence, supervised learning, unsupervised learning, deep learning, neural networks, data science, ML algorithms, predictive analytics, future of machine learning
Machine Learning (ML) is a branch of Artificial Intelligence (AI) that enables computers to learn from data without being explicitly programmed. By using ML algorithms, machines can improve their performance on tasks over time by identifying patterns and making decisions based on data.
At its core, machine learning involves feeding data into algorithms that process and learn from that data. The typical process includes:
Data Collection – Gathering relevant data from various sources.
Data Preparation – Cleaning and organizing the data for training.
Model Training – Applying algorithms to the data to learn patterns.
Model Evaluation – Testing the model’s performance.
Prediction or Decision Making – Using the trained model to make real-world predictions or decisions.
In supervised learning, the model is trained on a labeled dataset. This means the algorithm knows the correct answers and learns to predict the outcome from the input data. Popular algorithms include:
Linear Regression
Logistic Regression
Support Vector Machines
Decision Trees
Unsupervised learning involves training models on unlabeled data. The system tries to find hidden patterns or intrinsic structures in the data. Key techniques include:
Clustering (e.g., K-Means)
Dimensionality Reduction (e.g., PCA)
In reinforcement learning, an agent learns to make decisions by performing actions and receiving feedback from its environment. It's widely used in robotics, gaming, and autonomous systems.
Deep Learning is a subset of machine learning that uses neural networks with many layers (deep neural networks). This approach mimics the h
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