Regression analysis is a way of mathematically sorting out which variables have an impact, which variables matter most, and how the variables interact with each other. Linear regression is used when you have predictors that have a linear relationship with your target dependent variable. Logistic regression is where the dependent variable is categorical.
A classification model attempts to draw some conclusion from observed values. Machine learning classification models can predict or estimate the response variable or class given input data.
Classification algorithms include Decision trees, Random forest, Artificial Neural Networks, Naive Bayes, Support Vector Machines and Logistic Regression.
Deep learning is a subset of machine learning where artificial neural networks (algorithms inspired by the human brain) learn from large amounts of data. Deep learning is a more complex algorithm used for classifying images, identifying objects in images, and enhancing images, sensor data and signals. Deep learning models takes time to train such as deep neural networks and reinforcement learning.
Natural language processing strives to build machines that understand and respond to text or voice data—and respond with text or speech of their own—in much the same way humans do. NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly—even in real time and extract ever more accurate meaning from huge volumes of raw, unstructured, and unlabeled text & voice data sets.
Time series data is data that is recorded over consistent intervals of time. Time series forecasting is predictive analytics that can help businesses understand the underlying causes of trends or systemic patterns over time. Machine Learning models can analyze time series data to extract meaningful patterns in time and make accurate future predictions.