- Definition of Machine Learning
- Application of Machine Learning
- Activation Function
- Supervised Learning
- Unsupervised Learning
- Exploratory Data Analysis
- Simple Linear Regression
- Multiple Linear Regression
- Univariate Logistic Regression
- Multivariate Logistic Regression
- Introduction to Naive Bayes
- Naive Bayes for Categorical Data
- Naive Bayes for Text Classification
- Support Vector Machines
- Introduction to Decision Trees
- Introduction to Clustering
- K Mean Clustering
- K Mode Clustering
- DB Scan Clustering
- Pricipal Component Analysis
Machine Learning
Supervised Learning
Unsupervised Learning
Application of Machine Learning
There are several uses for machine learning in numerous sectors and fields. Here are a few typical uses for machine learning:
- Predictive Analytics: Machine learning algorithms can analyze time series data to make predictions and forecasts(Time series data are observations that have been gathered sequentially over time). It is used in sales forecasting, demand planning, stock market analysis, and predictive maintenance in industries like manufacturing and healthcare.
- Detection of Fraud Machine learning is being used to identify fraudulent activities by analyzing patterns and anomalies in financial transactions. It can be used in credit card fraud detection, insurance claim fraud detection, and cybersecurity.
- Recommendation Systems: Machine learning algorithms are being used to develop recommendation systems that provide personalized suggestions to users. Examples include product recommendations on e-commerce platforms, movie or music recommendations on streaming platforms, and content recommendations on social media platforms.
- Image Classification and object detection: Machine learning is used for computer vision-related tasks such as object detection and classification, facial recognition, image classification ,Video surveillance . Self-driving automobiles, medical image analysis etc
- Healthcare: Machine learning is employed in various healthcare applications and Public Health Surveillance, such as medical diagnosis, drug discovery, personalized medicine, and patient monitoring. It can help identify disease patterns, predict treatment outcomes, and improve patient care. Machine learning algorithms are used to analyze patient data, such as bio-medical images(X Ray,UltraSound,CT Scan etc) and signas(ECG ,EEG ,EMG etc), lab results, and patient records, to assist in diagnosing diseases. For example, machine learning models have been developed to detect cancer from medical imaging data, such as mammograms or CT scans, with high accuracy.
×
![]()