ML 1.5 Learning or working process of machine

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 v  Learning (or) working process of machine: -

Ø A machine learning algorithm works by learning patterns and relationships from data to make predictions or decisions without being explicitly programmed for each task.

Ø Learning process of machine learning algorithm works:

1.      Data Collection

2.      Data Preprocessing

3.      Choosing a right Model

4.      Training the Model

5.      Evaluating the Model

6.      Fine-tuning and Optimizing

7.      Prediction and Deployment

 

1)  Data Collection: -

Ø First, relevant data is collected. Data can be collected from various sources such as databases, text files, numeric data, images, audio files, etc from the web.

Ø This data could include examples, features, or attributes that are important for the task.

Ø This process involves organizing the data in a suitable format, such as a CSV file or a database, and ensuring that the data is relevant to the problem you're trying to solve.

Ø Defines the data points or objects to build the datasets.

Ø "What shape is it?  What color is it? Does it contain numbers or text?".

 

2)  Data Preprocessing: -

Ø Before feeding the data into the algorithm, it often needs to be pre-processed.

Ø This step may involve cleaning the data (removing duplicates, correcting errors), handling missing data (either by removing it or filling it in), and transforming the data (normalizing the data, scaling the data to a standard format) and splitting it into training and test sets.

Ø Preprocessing improves the quality of your data and ensures that your machine learning model can interpret it correctly. 

 

3)  Choosing a right Model: -

Ø Select an appropriate model for best performance. (supervised, un-supervised or reinforcement learning model)

Ø There are many types of models to choose from, including linear regression, decision trees, and neural networks. The choice of model depends on the nature of your data, size of your data, and the problem you're trying to solve.

Ø Depending on the task (e.g., classification, regression, clustering), a suitable machine learning model is chosen.

Ø Examples include decision trees, neural networks, support vector machines, and more advanced models like deep learning architectures.

 

4)  Training the Model: -

Ø Training involves feeding the data into the model and allowing it to adjust its internal parameters to better predict the output.

Ø The selected model is trained using the training data.

Ø During training, the algorithm learns patterns and relationships in the data. This involves adjusting model parameters iteratively to minimize the difference between predicted outputs and actual outputs (labels or targets) in the training data.

Ø During training, it's important to avoid overfitting (where the model performs well on the training data but poorly on new data) and underfitting (where the model performs poorly on both the training data and new data).

 

5)  Evaluating the Model: -

Ø Once a model is trained, the model is evaluated using the test data to assess its performance.

Ø Common metrics for evaluating a model's performance include accuracy (for classification problems), precision and recall (for binary classification problems), and mean squared error (for regression problems) that generalizes to new, unseen data

 

6)  Fine-tuning and Optimizing: -

Ø In this step, improve prediction accuracy by tuning hyperparameters.

Ø Models may be fine-tuned by adjusting hyperparameters (parameters that are not directly learned during training, like learning rate or number of hidden layers in a neural network) to improve performance.

Ø Techniques for hyperparameter tuning include grid search (where you try out different combinations of parameters) and cross validation (where you divide your data into subsets and train your model on each subset to ensure it performs well on different data).

 

7)  Prediction and Deployment: -

Ø Finally, the trained model is used to make predictions or decisions on new data. This process involves applying the learned patterns to new inputs to generate outputs, such as class labels in classification tasks or numerical values in regression tasks.

Ø Deploying a machine learning model involves integrating it into a production environment, where it can deliver real-time predictions or insights. 

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