Hey there 👋 , Welcome to world of Machine learning where you can never stop learning new algorithms. But for understanding advanced algorithms you need to start from basics. So, Let's list down systematically what are the necessary machine learning algorithms which can add some weapons to your arsenal.

Machine Learning Algorithms

So, most fundamentally ML can be categorized based on the learning approach, data. Let's focus on learning approach first as it is most basic and understandable. There are mainly 3 types of categories , but I am covering two of those Supervised ML and Unsupervised ML.

Supervised Learning

Supervised learning can be said as a learning where the algorithm is fed with data, also the prediction output. Based on the some calculations it figures out the relationship between the data and prediction output. Again the supervised learning can be divided into two types based on the prediction output. If the output is continues numbers, then it is called as regression. If the output is in categories then it is called as classification. Now without further delay, let me list down the algorithms

• Ordinary Least Squares Regression (Regression)
• Linear Regression (Regression)
• Ridge Regression (Regression)
• LASSO Regression (Regression)
• Elastic Net (Regression)
• Logistic Regression (Classification)
• Polynomial Regression (Regression)
• SVM (Regression & Classification)
• Naive Bayes (Majorly Classification)
• kNN (Regression & Classification)
• Decision Tree (Regression & Classification)
• Random Forest (Regression & Classification)
• Adaboost (Regression & Classification)
• GBDT (Regression & Classification)
• XGBoost (Regression & Classification)

Unsupervised Learning

In Unsupervised machine learning, the data is given to the algorithm but the predictions are unknown to the algorithm. This makes it different from supervised ML. Let me list down some popular

• PCA
• K-Means Clustering
• Clustering Variants (like C-means, K-medians etc)
• DBScan

Deep Learning

Deep learning is a subset of machine learning where neural networks, algorithms inspired by the human brain is used to learn the stuff. Although most of the algorithms comes under Supervised, this really requires a dedicated one.. So here it is

• Artificial Neural Network (ANN)
• Convolutional Neural Network (CNN)
• Recurrent Neural Network (RNN)
• Long short term memory (LSTM)
• Gated Recurrent Unit (GRU)
• Generative Adversarial Networks (GANs)
• AutoEncoder
• Attention based Models
• Transformer based Models

Cool ! That's it. We have seen almost all the major algorithms that are needed for this year. I am planning to make a series to explain these stuffs, so stay tuned 😉