### Logistic Regression

Back in the game after a hefty month of prepping and attending a conference, we're back with our #ML Snippets! Starting off February with a bang, we have Logistic Regression. Now let's start off with the basics - What is Logisitic Regression? Logistic regression predicts the probability that an instance belongs to a given class (binary classification). It’s commonly used for tasks like spam detection in emails. Unlike linear regression, which predicts continuous values, logistic regression produces probabilistic values between 0 and 1. The key idea is to use a logistic function (also known as a sigmoid function) to map input features to probabilities.

### Linear Regression

Happy New Year folks! For our first post of the New Year, we're going to step into Linear Regression, which is a part of Supervised Learning. 📊 Demystifying Linear Regression Linear Regression is a foundational algorithm in data science and plays a pivotal role in predicting continuous outcomes. It’s a statistical method that predicts the relationship between two variables by assuming a linear connection between the independent and dependent variables.

### A "Wading Depth" into Unsupervised Learning techniques - Clustering

Now that we've covered #supervisedlearning, it's time to move forward to #unsupervisedlearning. We'll be starting off with a basic 101 about clustering, followed by the other methods in our upcoming posts. Clustering is a type of unsupervised learning method in machine learning that involves grouping similar data points together based on their features or characteristics. The goal of clustering is to identify patterns or structures within the data that are not immediately apparent. It is usually used to draw references from datasets consisting of input data without labeled responses.

### A "Wading Depth" into Supervised Learning - Regression

Aight everybody, let's get straight into it. If you looked into our last post on Classification, you'll know that this post is about "Regression" in Machine Learning. However, if you're new here, howdy and welcome to our page. You can check out our last post here: https://lnkd.in/gucz4wAC Now onto Regression: Regression in machine learning is a statistical method used to model the relationship between a dependent (target) and independent (predictor) variables.It is a supervised learning technique which helps in finding the correlation between variables and enables us to predict the continuous output variable based on one or more predictor variables. It is mainly used for prediction, forecasting, time series modeling, and determining the causal-effect relationship between continuous variables.

### A "Wading Depth" into Supervised Learning - Classification

After quite the break with the festivites (phew!), we're here to give some more insights into Supervised Learning, mainly Classification and Regression. Regression will be covered in our next post so stay tuned for that. Until then, let's start some learning with Classification. Oh and in case if you're new here or would like a referesher on what we're talking about, check out our previous post on Supervised learning - https://lnkd.in/gEZxyvqA In supervised machine learning, algorithms can be broadly classified into regression and classification algorithms. Regression algorithms predict the output for continuous values, while classification algorithms predict categorical values.

### >A 101 on Unsupervised Learning

We're back with the 101 that you've been patiently waiting for - Unsupervised Learning. If you haven't checked our previous post on Supervised learning, you can catch up over here - https://lnkd.in/gEZxyvqA Unsupervised learning is a type of machine learning where models learn patterns exclusively from unlabeled data. Unlike supervised learning, where models are trained using labeled data, unsupervised learning models find hidden patterns and insights from the given data without any supervision. Here's a few pointers about unsupervised learning that simplifies our 101 for this post:- What it does: Unsupervised learning can't be directly applied to a regression or classification problem because we have our input data but no corresponding output data. The goal of unsupervised learning is to find the underlying structure of the dataset and group that according to similarities to represent that dataset in a compressed format..

### A 101 on Supervised Learning

In supervised learning, models are trained using labeled datasets – meaning that each segment of the data is labeled through and through and the model wouldn’t have a hard time comprehending this as all it would require is a bit of pre-processing or what we like to call “tweaking to make it prettier for the model.” To understand this better, we've added a visual below. Once the training process is completed, the model is tested based on test data (a subset of the training set), and then it predicts the output.

### Popular Posts

Supervised learning is a paradigm in machine learning where input objects and a desired output value, (a human-labeled supervisory signal) are used to train a model.

Supervised learning is a paradigm in machine learning where input objects and a desired output value, (a human-labeled supervisory signal) are used to train a model.