What is machine learning and why it is so important?
Machine learning has made possible to make machines learn. It is a field of computer science where machines or computers are made to work automatically without any external interruption and explicit programming. Applications of AI such as pattern recognition, self learning need some programming and algorithms by which it could become possible. Thus, building of algorithm includes deep study and this field is named as machine learning. Furthermore, construction of algorithms takes place for making AI applications real and accurate. After evolution of machine learning researchers come to know that machines can learn from data. This technology has made itself too far from traditional machine learning for which it was evolved. It has the ability to apply complex mathematical calculation automatically with self learning feature. It has proved itself with unbounded capacities.
Why machine learning?
It includes different categories of learning for different applications based on the target value or type. Machine learning models are first trained from training data. Further they learn from training and give precise results. The feature of self learning from past data is a great thing which makes machine learning different from other technologies. ML is important because it made those things possible which human can never think to do alone. Its technological improvements are so fast that it will accomplish all expectations of researchers. It enables analysis with massive amount of data. Computer programs are build which focus on learning from data by observation or experience. Patterns of data are memorised for future implementation which produces precise results. It allow machine to learn without any human intervention or action. For this different methods are used based on results required.
Machine Learning Methods:
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Supervised Learning:
In supervised learning method training of model is done with labeled data.
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Unsupervised Learning:
In unsupervised learning unlabeled training data is used to train the model.
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Semi-Supervised Learning:
In semi supervised learning system is trained with small amount of labeled data as well as very large size of unlabeled data.
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Reinforced Learning:
In reinforced learning model learn from behaviour of external environment.
Machine Learning Algorithms:
For different use cases there are different kinds of algorithms.Main types of algorithms includes:
- Linear Regression
- Logistic Regression
- SVM (Support Vector Machine)
- Naive Bayes
- Decision tree
- Random Forest
- KNN (K nearest Neighbour)
- K-means
- Gradient boosting
- Dimensionality reduction
All are common machine learning algorithms which can be applied to the data.
Machine Learning applications:
Machine Learning is used in industries across various purposes.
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Health care:
Machine Learning is used in Healthcare for many use cases. Medical practitioners are using an application of M.L for predicting medicinal usage for a particular patient. Machine Learning algorithms and techniques are used for decision making, disease prediction and health prediction and treatment.
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Finance:
M.L algorithms such as Bayesian Regression, Random forest and ridge regression has proved them best for making prediction system. For financial trading prediction system for stocks are built which can easily determine closing price of stocks. Many traders are using it for profitable results.
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Marketing and sales:
Recommendation system is an application of Machine Learning used by many marketing and e-commerce websites. The automatically provides the similar recommendations of products to a visitor for which anyone is looking for. This process increases the sale indirectly.
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Fraud Detection:
Image recognition, face detection and pattern recognition are used in daily life for fraud detection. Machine Learning model easily recognises fraud. Each person have their own pattern identification by which machine recognises them.
In final we can say that M,L is incredible breakthrough in field of AI. It is improving our life in many ways. This is why most organisations are going for machine learning services to improve their performance.
