Machine Learning Models: Reflecting the Past or Predicting the Future?
Introduction: Machine learning has transformed data analysis and prediction, but there is often confusion surrounding the ability of machine learning models to accurately predict the future. It is important to understand that these models are based on historical data and patterns, making them more reflective of the past rather than capable of accurately predicting the […]
Data Mining and Machine Learning
xploring the available datasets to find patterns and anomalies is known as data mining. The technique of learning from heterogeneous data in a way that may predict or forecast unknown / future values is known as machine learning. Together, these two ideas make it possible to depict historical data and anticipate future data. Data Mining […]
Data Preparation Challenges in Machine Learning
Machine learning initiatives can be very difficult. The quality and quantity of data that needs to be processed, the complexity of the underlying algorithms, and the requirement for accuracy and dependability can all contribute to their difficulty. Additionally, the development and implementation of machine learning applications can be costly and time-consuming. Despite the fact that […]
Gradient Descent
The gradient descent technique is a popular optimization method (to find minimum error) in deep learning and machine learning. Gradient descent modifies the parameters in order to minimize specific functions. Backward propagation technique is used in deep learning whereas weights and biases are optimized in linear regression. A gradient calculates how much a function’s output […]
More About AutoML
Automated Machine Learning offers techniques and procedures to make Machine Learning accessible to those who are not specialists in it, to increase its effectiveness, and to quicken the pace of Machine Learning research. Basically, Automated machine learning (AutoML) automates and eliminates manual steps required to go from a data set to a predictive model. AutoML […]
A Brief About AutoML
Automated machine learning basically involves choosing the model algorithm, optimising the hyperparameters, modelling iteratively and evaluating the model. Instead of trying to replace data scientists, this technology hopes to relieve them of tedious work. Parts of the data science workflow are being automated using AutoML in an effort to promote data-driven decision making. AutoML aims […]
Data Mining and Machine Learning
Exploring the available datasets to find patterns and anomalies is known as data mining. The technique of learning from heterogeneous data in a way that may predict or forecast unknown / future values is known as machine learning. Together, these two ideas make it possible to depict historical data and anticipate future data. Data Mining […]
Underfitting and Overfitting in Machine Learning
The two main issues that affect machine learning and lower the effectiveness of the machine learning models are overfitting and underfitting. A model is considered to be a good machine learning model if it appropriately generalises any new input data from the problem area. This helps us to make predictions about future data, that the data model has […]