Machine Learning is a subset of artificial intelligence. It focuses mainly on the designing of systems, thereby allowing them to learn and make predictions based on some experience which is data in the case of machines.
Regularization is a technique that shrinks the coefficient estimates towards zero. This technique adds a penalty to more complex models and discourages learning of more complex models to reduce the chance of overfitting.
NLTK(Natural Language Toolkit) is the most popular and widely used python library for doing Natural Language Processing(NLP) or Text Mining.
The important topics in machine learning such as bias, variance, overfitting, underfitting, precision, recall, f1-score, and so on.
Handle imbalance datasets using under-sampling, oversampling, and a mixture of both with SMOT, NeaMiss, RandomOverSampler, and RandomUnderSampler.
K-Nearest Neighbor(KNN) is a supervised algorithm in machine learning. It is used for classification and regression analysis.
k-means clustering is centroid-based clustering in machine learning. It is an easy method for clustering similar data in machine learning
Hyperparameter tuning is an essential term in the field of machine learning. Hyperparameter is a set of parameters that control the learning process of the algorithm.
Feature scaling in machine learning is a crucial step. Standardization and normalization are the two most common methods to scale the features.
Null values in dataset are the empty field represented as NaN(Not a Number). Null value do not mean zero value, actually it is an empty field. Datasets that are available for preparing machine learning model may contain some null values in it.
