Machine Learning Basics

What is Machine Learning?

Machine Learning is defined as a set of methods that can automatically detect patterns in data, and then use the uncovered patterns to predict future data, or to perform other kinds of decision making under uncertainty. 

Types of Machine Learning

Machine learning is usually divided into two main types.

1. Predictive or supervised learning approach -

The predictive or supervised approach in machine learning is a type of machine learning where the algorithm is trained on a set of labeled data. The labeled data contains the input data and the corresponding output data. The algorithm learns to predict the output for new input data based on the patterns it has learned from the training data.

Predictive or supervised machine learning algorithms are often used to solve classification and regression problems.

Classification problems -

 involve predicting a categorical output variable, such as whether a customer will churn, whether an email is spam, or whether an image contains a cat or a dog.

Regression problems involve predicting a real-valued output variable, such as the price of a house, the number of customers who will visit a store on a given day, or the risk of a customer defaulting on a loan.

Here are some examples of predictive or supervised machine learning algorithms:

• Logistic regression

• Support vector machines

• Decision trees

• Random forests

• Gradient boosting machines

Predictive or supervised machine learning algorithms are widely used in a variety of applications, such as:

• Fraud detection

• Risk assessment

• Medical diagnosis

• Product recommendation

• Customer churn prediction

• Marketing campaign optimization

Here is an example of how a predictive or supervised machine learning algorithm can be used to solve a classification problem:

Suppose we want to train a machine learning algorithm to predict whether a customer will churn. We can collect a dataset of customers, along with their corresponding churn labels (churned or not churned). The algorithm can then be trained on this dataset to learn to predict the churn label for new customers.

Once the algorithm is trained, we can use it to predict whether new customers are likely to churn. This information can then be used to take steps to prevent churn, such as offering customers special discounts or promotions.

Predictive or supervised machine learning algorithms are a powerful tool for solving real-world problems. However, it is important to note that these algorithms are only as good as the data they are trained on. If the training data is biased or inaccurate, the algorithm will learn to make biased or inaccurate predictions.

2. Descriptive or unsupervised learning

Descriptive or unsupervised learning is a type of machine learning where the algorithm is trained on a set of unlabeled data. The unlabeled data does not contain the output data. The algorithm learns to find patterns and insights in the data without any prior knowledge of the output.

Descriptive or unsupervised learning algorithms are often used to solve clustering and anomaly detection problems.

Clustering problems involve grouping similar data points together. For example, a clustering algorithm could be used to group customers together based on their purchase history.

Anomaly detection problems involve identifying unusual or unexpected data points. For example, an anomaly detection algorithm could be used to identify fraudulent transactions or network intrusions.

Here are some examples of descriptive or unsupervised learning algorithms:

• K-means clustering

• Hierarchical clustering

• Gaussian mixture models

• Principal component analysis

• Local outlier factor

Descriptive or unsupervised learning algorithms are widely used in a variety of applications, such as:

• Customer segmentation

• Fraud detection

• Network intrusion detection

• Medical diagnosis

• Recommendation systems

• Market research

Here is an example of how a descriptive or unsupervised learning algorithm can be used to solve a clustering problem:

Suppose we want to group customers together based on their purchase history. We can collect a dataset of customers, along with their purchase data. The algorithm can then be trained on this dataset to learn to cluster the customers together based on their purchase patterns.

Once the algorithm is trained, we can use it to group new customers into clusters. This information can then be used to target customers with relevant marketing campaigns or product recommendations.

Descriptive or unsupervised learning algorithms are a powerful tool for finding hidden patterns and insights in data. However, it is important to note that these algorithms are not able to provide explanations for their predictions. If we want to understand why the algorithm has made a particular prediction, we need to use other techniques, such as interpretable machine learning.