Machine_Learning_Pipeline_with_Code_Examples.pptx

Presentation

1. Machine Learning Pipeline A Simple & Easy Guide By: Your Name

2. Machine Learning Pipeline By: Zainab Murtaza A Simple & Easy Guide

3. What is Machine Learning? Machine Learning (ML) means teaching computers to learn from data. Your title here Presentations are communication tools that can be used as demonstrations. from sklearn.linear_model import LinearRegression Presentations are communication tools that can be used as demonstrations. Example: Predicting house prices or detecting spam. Presentations are communication tools that can be used as demonstrations. Code Example (Python):

4. What is Machine Learning? Machine Learning (ML) means teaching computers to learn from data. Example: Predicting house prices or detecting spam. Code Example (Python): from sklearn.linear_model import LinearRegression model = LinearRegression()

5. What is Machine Learning? Machine Learning (ML) means teaching computers to learn from data. Your title here Example: Predicting house prices or detecting spam. Your title here Presentations are communication tools Code Example (Python): from sklearn.linear_model import LinearRegression Your title here

6. Why We Need an ML Pipeline An ML pipeline organizes the process of building models. Benefits: • Automation • Reusability • Consistency

7. Step 1: Data Collection Gather data from various sources. Example: import pandas as pd data = pd.read_csv('data.csv') print(data.head())

8. Step 1: Data Collection Presentations are communication tools that can be used as demonstrations. Gather data from various sources. 01 Presentations are communication tools that can be used as demonstrations. Example: 02 Presentations are communication tools that can be used as demonstrations. import pandas as pd 03 Presentations are communication tools that can be used as demonstrations. data = pd.read_csv('data.csv') 04

9. Step 2: Data Preprocessing Clean and prepare the data before training. Example: from sklearn.preprocessing import StandardScaler scaler = StandardScaler() X_scaled = scaler.fit_transform(X)

10. Step 3: Model Training Train the model with clean data. Example: from sklearn.tree import DecisionTreeClassifier model = DecisionTreeClassifier() model.fit(X_train, y_train)

11. Step 4: Model Evaluation Test model accuracy and performance. Example: from sklearn.metrics import accuracy_score y_pred = model.predict(X_test) print(accuracy_score(y_test, y_pred))

12. Step 5: Deployment Deploy model to production. Example (saving model): import joblib joblib.dump(model, 'model.pkl')

13. Step 5: Deployment Deploy model to production. Example (saving model): import joblib

14. Step 6: Monitoring & Maintenance Track model performance and retrain if needed. Example: new_accuracy = accuracy_score(y_test, model.predict(X_test)) if new_accuracy < 0.8: retrain_model()

15. The title goes here Presentations are communication tools that can be used as demonstrations. Track model performance and retrain if needed. Presentations are communication tools that can be used as demonstrations. if new_accuracy < 0.8: Presentations are communication tools that can be used as demonstrations. Example: Presentations are communication tools that can be used as demonstrations. new_accuracy = accuracy_score(y_test, model.predict(X_test))

16. Summary ML Pipeline Steps: 1. Data Collection 2. Data Preprocessing 3. Model Training 4. Model Evaluation 5. Deployment 6. Monitoring Machine Learning = Continuous Learning!

17. Summary ML Pipeline Steps: 1. Data Collection 2. Data Preprocessing

18. Summary ML Pipeline Steps: 1. Data Collection 2. Data Preprocessing 3. Model Training