A Quick Introduction to Machine?Learning

Machine learning allows systems to learn from data and make decisions automatically with minimal human intervention. Whether it is guessing results, sorting things into groups or finding patterns, machine learning is becoming a very useful tool for working with data. I come from a BI background and have worked extensively with data. But now, I have come to realize the power of machine learning, which adds a whole new layer of intelligence to how data is processed and used. Let us try to break down how machine learning models work:

Thanks a lot, to the creator of this beautiful image that helped explain the process so easily!

1. Getting the?Data?

Every machine learning model starts with data which typically consists of Input Variables and Output Variables:

• Input variables (Features): These are the pieces of information that the model uses to make predictions or decisions. They are the independent variables or predictors. If you are trying to predict the price of a house, the input variables could be things like: Size of the house, bedrooms, location, age of house etc.
• Output variables (Target): This is the thing we want the model to predict or figure out based on the input data, such as the price of the house or whether someone gets a loan etc.

The first task is cleaning the data, which includes handling missing values, checking for outliers (bad records), and understanding its distribution.

2. Exploring the?Data?

Next, we explore the data to better understand it:

• We check if there are relationships (correlations) between different pieces of data. For example, does the size of a house affect its price? or does age affect whether someone gets a loan?
• We calculate important statistical metrics like mean, median, and standard deviation.
• We also identify any missing values and determine how to handle them.

3. Preparing the?Data?

Once we understand the data, we need to pre-process it to make it usable for the machine learning model:

• Scaling: Adjusts the values of features so that no variable dominates over others due to differences in their ranges. For example, if one feature like Square Footage has values from 500 to 5,000, and another like Number of Bedrooms ranges from 1 to 10, Square Footage will influence the model more. Scaling makes both features comparable, helping the model learn effectively. There are two most common methods are Min-Max Scaling and Standardization (Z-score normalization) to scale the numbers.
• Encoding: Converting categorical data (e.g., gender, education) into numbers so the computer can process them.

4. Splitting the?Data?

We split the data into two parts:

• Training data (70%): This is used to train the model. We use it to teach the model how to make predictions.
• Testing data (30%): This is kept aside to test the model and see how well it learned.
• It could be 80:20 also, depending on the requirement.

5. Teaching the?Model?

Now, we teach the computer using the training data. The model learns from examples using learning algorithms such as:

1. Random Forests

• Why it is popular: Random Forests are extremely versatile, powerful, and perform well on a wide range of tasks, including classification and regression. Their ability to handle large datasets and prevent overfitting makes them a go-to choice for many.

2. Support Vector Machines (SVM)

• Why it is popular: SVMs are highly effective for classification, especially in high-dimensional spaces. They are widely used in fields like text classification, image recognition, and bioinformatics.

3. Decision Trees

• Why it is popular: Decision Trees are easy to interpret and understand, making them great for beginners. They work well for classification and regression tasks but are often used as the base for more complex models like Random Forests and Gradient Boosting.

4. K-Nearest Neighbors (KNN)

• Why it is popular: KNN is simple and intuitive, making it a good starting point for many classification problems. It’s particularly useful for small datasets but can struggle with large datasets because of its computational complexity.

5. Logistic Regression

• Why it is popular: Despite its name, Logistic Regression is a classification algorithm. It is popular for binary classification problems (e.g., predicting whether something happens or not) because it is fast, interpretable, and effective for linearly separable data.

6. Fine Tuning the Model?

Model may need fine tuning which can be done using hyperparameters:

• Hyperparameters are the settings or configurations that you define before training a machine learning model. Hyperparameters are set manually and control the overall behavior of the model
• Hyperparameter optimization ensures the model is as accurate as possible and we adjust it to help the model learn better.

7. Testing the Model?

Once the model is trained, we test its performance on the Test data that was kept aside for testing. We check how well the model can make predictions on unseen data.

Performance metrics include:

• Accuracy: How often the model makes correct predictions.

8. Cross Validation?

To ensure the model is not just memorizing the data (overfitting), we use cross validation. This involves training the model multiple times on different subsets of the data and testing it on the rest.

9. Making Predictions?

Once the model is trained and tested, it is ready to make predictions. For example, we input someone’s age and income and see if the model predicts they will get a loan or not.

10. Evaluating the Model?

Once we have built and trained a machine learning model, it is time to see how well it works. Evaluation is like testing how good the model is at making predictions. We need to check if it is making accurate predictions or if it needs improvement. We use popular metrics to evaluate its performance:

• Accuracy: This tells us how often the model is correct. It is a simple metric but works well when the data is balanced (positive and negative cases are about the same).
• Sensitivity and Specificity: These metrics tell us how well the model detects different outcomes:
• Sensitivity (Recall): How well it detects positive outcomes (e.g., people who should get the loan).
• Specificity: How well it detects negative outcomes (e.g., people who shouldn’t get the loan).
• RMSE and R2: These are used for models that predict continuous values (like predicting someone’s salary or the price of a house):
• RMSE (Root Mean Square Error) measures how far off the model’s predictions are from the actual values.
• R2 (R-squared) tells us how well the model explains the variation in the data.

Machine learning is all about learning from data, making predictions, and improving those predictions over time. While the terminology may sound technical, at its core, it is just like teaching a computer to make decisions based on examples. There is a lot to learn in Machine Learning and in this article we have covered is just the tip of the iceberg.

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