Machine Learning Models

 Machine Learning 

 

  
What is Machine Learning ?

Machine learning (ML) is a type of artificial intelligence (AI) that allows computers to learn and improve from experience. It uses algorithms to analyze data and make predictions  

For more Details: Machine Learning

What is Components of Machine Learning Models ?

The core components of machine learning models are data, algorithms, models, and predictions

* Data

Data collection and ingestion: Gathering and bringing in data for use in machine learning 

Data preprocessing and transformation: Preparing data for use in machine learning

* Algorithms 

Supervised learning: Uses labeled datasets to train algorithms to classify data or predict outcomes 

Semi-supervised learning: Uses a combination of labeled and unlabeled data to build models  

Random forest: Builds multiple decision trees on randomly selected samples from the data 

 

Machine Learning Models 

* Naive Bayes :-

 A supervised learning algorithm that uses conditional probabilities to create predictive models 

* Linear regression :-

 A supervised learning algorithm that performs a regression task 

WHY MACHINE LEARNING 

WORKS WITH ARTIFICIAL 

INTELLIGENCE ?

Artificial intelligence (AI) with machine 

learning (ML) is a combination of 

technologies that allows computers to 

learn and improve their performance over 

time 

 How it works :

 # AI systems use data to learn and improve their performance 

# ML algorithms analyze data, learn from it, and make decisions

# The more data used, the better the model will get . 

# AI systems can be used to perform tasks like recognizing images, translating languages, and more

 

Benefits  :

# AI and ML can help businesses reduce costs and increase operational efficiency.

AI and ML can help consumers receive more personalized services. 





 



 






  • Semi-supervised learning: Uses a combination of labeled and unlabeled data to build models 

  • Random forest: Builds multiple decision trees on randomly selected samples from the data 

  • Data collection and ingestion: Gathering and bringing in data for use in machine learning
  • Data preprocessing and transformation: Preparing data for use in machine learning

  • Data collection and ingestion: Gathering and bringing in data for use in machine learning
  • Data preprocessing and transformation: Preparing data for use in machine learning

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