Machine_Learning_Pipeline_Presentation.pptx

Ml

1. Machine Learning Pipeline By: Zainab Murtaza

2. What is Machine Learning? Machine Learning (ML) is when computers learn from data instead of being directly programmed. Example: Predicting house prices or detecting spam emails.

3. Why We Need an ML Pipeline An ML pipeline helps organize and automate the steps of building a model. It makes the process faster, repeatable, and reliable.

4. Step 1: Data Collection Gather data from various sources like files, sensors, or websites. Example: Collecting sales data, images, or customer feedback.

5. Step 1: Data Collection Presentations are communication tools that can be used as demonstrations. Gather data from various sources like files, sensors, or websites. Presentations are communication tools that can be used as demonstrations. Example: Collecting sales data, images, or customer feedback.

6. Step 2: Data Preprocessing Clean and prepare the data before training. • Handle missing values • Remove duplicates • Convert text to numbers

7. Step 3: Model Training Choose an algorithm and train the model using your data. Example: Decision Trees, Linear Regression, Neural Networks.

8. Step 4: Model Evaluation Test the model on unseen data to check accuracy and performance. Example: Confusion Matrix, Accuracy Score, Precision, Recall.

9. Step 5: Deployment Put the model into a real-world system where it can make predictions. Example: Recommender systems, chatbots, fraud detection.

10. Step 6: Monitoring & Maintenance Keep checking model performance over time. Update or retrain it when new data arrives or performance drops.

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