Machine learning is an application of artificial intelligence that provides computer systems the ability to learn automatically without being explicitly programmed. It has brought a revolution in the field of data analysis.
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Feed a set of data to the system, clean it for any missing or null data, convert all the values to numerical digits, develop and evaluate the model. Check the accuracy of the model and improve it. Once the desired accuracy is achieved you are ready to make correct predictions.
Lets see some of the fields (examples) where machine learning is being used in data science.
- Cancer Detection - Whether the Cancer is benign or malignant.
- Classifying whether the received mail is spam or not.
- Online Fraud Detection
- Video Surveillance
- Traffic Predictions
- Face Recognition
- Online Customer Support
- Search Engine Result Refining
- Predicting Economic Trends
- Recommendation Engines
- Self-Driving Cars
- Predicting when patients gets sick,
- Ranking of post on social media
- Making sales and marketing more efficient
- Credit Card Fraud Detection and many more
Lets see the systems or techniques behind it. Different problems have different solutions so machine learning too has different techniques for different situations.
Lets see some of them.
Regression
Regression is a machine learning algorithm based on supervised learning. Regression models are used to predict a continuous output. It is mostly used to find the relationship between the variables and the forecasting. Example- Predicting the house prices for features such as it's area, locality, number of bedrooms and toilets, building material used, proximity to market and so on.We have more than one regression technique as for different situations we require different approach or methods.
Classification
Classification is a type of supervised learning. It specifies the class to which data elements belong to and is best used when the output has finite and discrete values. The computer program learns from the data input given to it and uses this learning to classify new observation.Some examples of classification problems are:
- Handwriting Recognition
- Speech Recognition
- Document classification
- Bio metric identification
Clustering
Clustering is a machine learning technique that involves the grouping of data points. For a given set of data points, we can use clustering to classify each data point into specific group. Clustering is a method of unsupervised learning and a common technique for statistical data analysis used in many fields.
Association
Association is a method of discovering interesting relationships between variables in large databases. It is a rule based machine learning and data mining technique. It identifies the hidden correlations in databases by applying some measures of interestingness to generate an association rule for new searches.
Example: Grocery Shopping
Anomaly Detection
Anomaly detection is referred to the identification of items or events that do not confirm to an expected pattern or the discovery of abnormal and unusual activities.
Examples: Credit card fraud detection, contextual anomalies.
Sequence Mining
It is concerned with finding statistical relevant patterns between data examples where the values are delivered in a sequence.
Example: Predicting Next Event, Click Stream.
Dimension Reduction
Dimension reduction is the process of reducing the number of random variables under consideration by obtaining a set of principal variables.
Example: Reducing the size of data (Principal Component Analysis (PCA))
Recommendation System
In Recommendation system, the recommendation engines basically are data filtering tools that make use of algorithms and data to recommend the most relevant items to a particular user.
Example: When we watch the movies on Netflix, we then get the recommendations from Netflix of the same types of movies which we watch.
Similarly, when we buy products on Amazon and Flipcart we then get recommendation of the similar products from them.
These are only few to mention. Keep visiting for more informative stuffs.
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