What is machine learning and how does it work ?
Machine learning is a branch of (AI) artificial intelligence that enables computers to learn from data and make decisions or predictions without being explicitly programmed. It focuses on creating algorithms that can automatically detect patterns, make decisions, and improve over time with experience.
Machine learning works by building models from data. Here's a simplified explanation of the process:
1. Data Collection
Gathering Data: The first step is to collect a large amount of data relevant to the problem you're trying to solve. This could be anything from images, text, or numerical data, to more complex data like time-series or audio recordings.
2. Data Preprocessing
Cleaning the Data: Raw data often has noise, missing values, or inconsistencies. This step involves cleaning and transforming the data into a format suitable for analysis.
Feature Selection: Identifying the most important variables (features) in the data that will help the model make accurate predictions.
3. Splitting the Data
Training Set: The majority of the data is used to train the machine learning model. The model learns patterns and relationships from this data.
Test Set: A smaller portion is used for testing the model’s performance, ensuring that it generalizes well to unseen data.
4. Selecting a Machine Learning Algorithm
Supervised Learning: Used when you have labeled data (input and correct output). Algorithms like linear regression, decision trees, and support vector machines (SVM) are popular choices.
Unsupervised Learning: Used when you have unlabeled data and the model tries to find hidden patterns. Examples include clustering (e.g., K-Means) and association algorithms.
Visit more- Machine Learning Course in Pune
https://www.sevenmentor.com/machine-learning-course-in-pune.php
Machine learning is a branch of (AI) artificial intelligence that enables computers to learn from data and make decisions or predictions without being explicitly programmed. It focuses on creating algorithms that can automatically detect patterns, make decisions, and improve over time with experience.
Machine learning works by building models from data. Here's a simplified explanation of the process:
1. Data Collection
Gathering Data: The first step is to collect a large amount of data relevant to the problem you're trying to solve. This could be anything from images, text, or numerical data, to more complex data like time-series or audio recordings.
2. Data Preprocessing
Cleaning the Data: Raw data often has noise, missing values, or inconsistencies. This step involves cleaning and transforming the data into a format suitable for analysis.
Feature Selection: Identifying the most important variables (features) in the data that will help the model make accurate predictions.
3. Splitting the Data
Training Set: The majority of the data is used to train the machine learning model. The model learns patterns and relationships from this data.
Test Set: A smaller portion is used for testing the model’s performance, ensuring that it generalizes well to unseen data.
4. Selecting a Machine Learning Algorithm
Supervised Learning: Used when you have labeled data (input and correct output). Algorithms like linear regression, decision trees, and support vector machines (SVM) are popular choices.
Unsupervised Learning: Used when you have unlabeled data and the model tries to find hidden patterns. Examples include clustering (e.g., K-Means) and association algorithms.
Visit more- Machine Learning Course in Pune
https://www.sevenmentor.com/machine-learning-course-in-pune.php
What is machine learning and how does it work ?
Machine learning is a branch of (AI) artificial intelligence that enables computers to learn from data and make decisions or predictions without being explicitly programmed. It focuses on creating algorithms that can automatically detect patterns, make decisions, and improve over time with experience.
Machine learning works by building models from data. Here's a simplified explanation of the process:
1. Data Collection
Gathering Data: The first step is to collect a large amount of data relevant to the problem you're trying to solve. This could be anything from images, text, or numerical data, to more complex data like time-series or audio recordings.
2. Data Preprocessing
Cleaning the Data: Raw data often has noise, missing values, or inconsistencies. This step involves cleaning and transforming the data into a format suitable for analysis.
Feature Selection: Identifying the most important variables (features) in the data that will help the model make accurate predictions.
3. Splitting the Data
Training Set: The majority of the data is used to train the machine learning model. The model learns patterns and relationships from this data.
Test Set: A smaller portion is used for testing the model’s performance, ensuring that it generalizes well to unseen data.
4. Selecting a Machine Learning Algorithm
Supervised Learning: Used when you have labeled data (input and correct output). Algorithms like linear regression, decision trees, and support vector machines (SVM) are popular choices.
Unsupervised Learning: Used when you have unlabeled data and the model tries to find hidden patterns. Examples include clustering (e.g., K-Means) and association algorithms.
Visit more- Machine Learning Course in Pune
https://www.sevenmentor.com/machine-learning-course-in-pune.php
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