Some Pointers

• Machine Learning is usually a small piece of a big project.
• Typically 10-15% of the time is spent on ML.
• A lot more time is spent in capturing and processing data for ML and taking decisions based on the ML output.

Steps in ML Project

1. Look at the big picture
2. Get the data
3. Discover and visualise the data to gain insights.
4. Prepare the data for Machine Learning Algorithms.
5. Select a model and train it.
6. Fine-tune your model.
7. Present your solution.
8. Launch, monitor and maintain your system.

Step 1: Look at the Big Picture

1. Frame the problem
   1. What are input and output?
   2. What is the business objective?
   3. What is the current solution?
2. Select a performance measure
   1. Regression
      1. Mean Squared Error (MSE)
      2. Mean Absolute Error (MAE)
   2. Classification
      1. Precision
      2. Recall
      3. F1 Score
      4. Accuracy
3. List and check the assumptions

Step 2: Get the Data

1. Check data samples