AI Model Training - Basic to Advanced

AI model training is an essential part of machine learning. यह प्रक्रिया data preprocessing, model selection, training, evaluation, और deployment को cover करती है।

1. Introduction to AI Model Training

AI model training involves using algorithms to learn patterns from data. यह प्रक्रिया supervised, unsupervised, और reinforcement learning पर आधारित हो सकती है।

2. Data Preprocessing

# Example of data preprocessing using Python
import pandas as pd
from sklearn.preprocessing import StandardScaler

data = pd.read_csv('dataset.csv')
scaler = StandardScaler()
data_scaled = scaler.fit_transform(data)
    

3. Selecting a Model

Choosing the right model is crucial. Common models include Decision Trees, Neural Networks, और Support Vector Machines.

4. Training the Model

# Training a simple machine learning model
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

data_train, data_test, labels_train, labels_test = train_test_split(data, labels, test_size=0.2)
model = RandomForestClassifier()
model.fit(data_train, labels_train)
    

5. Evaluating the Model

# Model evaluation using accuracy
from sklearn.metrics import accuracy_score

predictions = model.predict(data_test)
accuracy = accuracy_score(labels_test, predictions)
print("Model Accuracy:", accuracy)
    

6. Deploying the Model

Deploying an AI model involves integrating it into a web application or API.

# Deploying model using Flask
from flask import Flask, request, jsonify
import pickle

app = Flask(__name__)
model = pickle.load(open('model.pkl', 'rb'))

@app.route('/predict', methods=['POST'])
def predict():
    data = request.get_json()
    prediction = model.predict([data['features']])
    return jsonify({'prediction': prediction.tolist()})

if __name__ == '__main__':
    app.run(debug=True)
    

7. Best Practices

1. Use clean and well-labeled data.
2. Perform feature engineering.
3. Optimize hyperparameters.
4. Monitor model performance over time.
5. Deploy models with version control.

8. Conclusion

AI model training is a continuous process. नई techniques और algorithms को सीखते रहना महत्वपूर्ण है। Keep experimenting and improving your models!