“Mastering Machine Learning: A Journey Through Algorithms” – It’s about understanding how they work, implementing them, and knowing when to use which. In this post, I’ll summarize my journey and share resources for further learning, hoping to inspire and assist you on your own journey
First of all a warm welcome to the fascinating world of machine learning algorithms 🙂
Machine learning is all about teaching computers to learn from data and make decisions or predictions. For instance, a machine learning algorithm can predict the price of a house based on factors like its size, location, and age. Or can highlight factors responsible for your increase in weight over a period of time (provided if you define all parameters/routine without hiding ;))
Types of Machine Learning
Our journey begins with understanding the three main types of machine learning: supervised learning, unsupervised learning, and semi-supervised learning.
Supervised Learning: In this type of learning, we have labeled data. A simple as having a guide or a teacher who guides the learning process. For instance, if we want to predict whether an email is spam or not, we would train our algorithm with a set of emails that are already labeled as ‘spam’ or ‘not spam’. And another such example is – loan defaultee! — predicting whether a loan applicant is likely to default on their loan payments.
Unsupervised Learning: This type of learning deals with unlabeled data. You will be given a set of data points and the algorithm has to find patterns and relationships in the data on its own. A common example is customer segmentation in marketing where we group customers based on their purchasing behavior or their demographics or their preferences!
Semi-supervised Learning: This is a mix of the two previous types. We have a small amount of labeled data and a large amount of unlabeled data. The algorithm learns from the labeled data and then applies that knowledge to the unlabeled data.
Linear and Nonlinear Algorithms
Next, we look into the world of linear and nonlinear algorithms.
Linear Algorithms: These algorithms, like linear regression (I will discuss about it in another article in detail along with the mathematics behind it), assume a linear relationship between the input and output variables. If we were predicting house prices, a linear algorithm would assume that as the size of the house increases, the price would increase at a constant rate. The classic example of y=mx+c! (if you remember the the equation of a straight line)
Nonlinear Algorithms: These algorithms, like decision trees, do not make this assumption. They can capture more complex relationships between variables. For example, a decision tree could capture that while the price of a house generally increases with size, it might decrease if the house is in a bad location.
Ensemble Algorithms
Finally, we explore ensemble algorithms, which combine multiple machine learning models to make a prediction. It’s like asking a group of experts for their opinion and then making a decision based on their collective wisdom. An example is the random forest algorithm, which combines multiple decision trees to make a prediction.
We will discuss in detail related to these algorithms using a specific example along with their limitations, assumptions etc. Mastering machine learning algorithms is a journey, not a destination. There’s always more to learn, more algorithms to explore, and more problems to solve. Keep following datavedha LinkedIn page and Happy learning!
