Plus/minus, positive/negative, True/False, and 0/1 these all are the concept of binary outcomes in the form of result which are normally used in Machine learning, Data mining using related programming languages. Today the user around the globe using different application which are based on Artificial Intelligence, and related smart machines. These machines are so smart and accurate for prediction and assumption in the form of IOT, AI base Robot and others. In the field of Data Science Machine learning and Data mining, these are the core field to find the knowledge base pattern from the existing data/database, for a long time smart machine based software has these features to make smart to smartest machines, but only few of folk know about the differences between these two very similar word Machine learning AND Data Mining!
Majority of them thinking both are same because almost different algorithm like (Regression, Classification) are exist in both.
Fahad Hussain Tutorial CLICK here.
So, in this Article we are going to understand about the differences and scope of Machine learning and Data Mining
Here we go!
Machine Learning:
Machine learning is the subpart of Artificial Intelligence also include in the latest trending field of Data Science, So, what is ML: Machine learning is the study of powerful algorithm and statistical model that increase the ability of computer in term of working/performance without using explicit instruction, where ML divided into three parts:
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Supervised Learning is the field for labeled data for predication and classification them using Regression and it different types, and classify using different ML Algorithm like (SVM, RF etc...). Unsupervised Learning is the field for unlabeled data for predication and clustering them using different types of clustering, Algorithm like (SVM, RF etc...), whereas in Reinforcement learning the ability of learning is going to move ahead here we train out machine using plenty to avoid mistake again by using these technique machine able to work smart than before.
The journey of Machine learning shows it is the collection statistical model base algorithm to make you machine smart in term of prediction and analysis. ML is not a new technology, first introduced in 1950 till now a lot of modern algorithms make their place in ML domain. ML used in different domains as well like Fraud Detection, Data Analysis, and many more.
Data Mining:
Data mining (DM), is the process of finding/Discovering the designing pattern from large amount of data set by using different algorithm including the machine learning, statistical and database, therefore it is also called knowledge discovery in databases (KDD).
In simple word, in this process we are finding those data set which is invisible but inherited from actual data set that is accurate and useful for us after discovering.It is an iterative process of creating a predictive and descriptive model, by uncovering previously unknown trends and pattern in vast amounts of data in order to support decision making. Also, DM applied in the limited area than machine learning because its domain big. Quite often, the data set in Data Mining is massive, complicated, and/or may have special problems (such as there are more variables than observations).
Now it is time to compare them in term of working, performance and importance':
1. It is used for extracting knowledge/information and describing about pattern on it from a big amount of data WHEREAS it is used to predict discrete/continuous value using different statistical and modern algorithm.
2. The concept of Data Mining introduced in 1930 WHEREAS it is introduced in 1950.
3. Its origin based on unstructured data/data set WHEREAS it is not necessary only unstructured data will be use.
4. Generally used to identify the classification and clustering between/among different pattern WHEREAS it is not only used for that purpose also for fraud deduction, spam mail, disease identification etc.
5. It is based/abstracted from Data warehousing that's why another course named Data mining and data warehousing also offered in BS/MS program of CS, WHEREAS it is based on data/machine.
6. The scope or domain of Data Mining is not enough than Machine learning WHEREAS its area is vast in term of usage.
Author, Fahad Hussain MSCS, MCS
Fahad Hussain Tutorial CLICK here.
Voluntary work as a Computer Scientist, to explore the difficulties as simple words!!!
Majority of them thinking both are same because almost different algorithm like (Regression, Classification) are exist in both.
Fahad Hussain Tutorial CLICK here.
So, in this Article we are going to understand about the differences and scope of Machine learning and Data Mining
Here we go!
Machine Learning:
Machine learning is the subpart of Artificial Intelligence also include in the latest trending field of Data Science, So, what is ML: Machine learning is the study of powerful algorithm and statistical model that increase the ability of computer in term of working/performance without using explicit instruction, where ML divided into three parts:
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Supervised Learning is the field for labeled data for predication and classification them using Regression and it different types, and classify using different ML Algorithm like (SVM, RF etc...). Unsupervised Learning is the field for unlabeled data for predication and clustering them using different types of clustering, Algorithm like (SVM, RF etc...), whereas in Reinforcement learning the ability of learning is going to move ahead here we train out machine using plenty to avoid mistake again by using these technique machine able to work smart than before.
The journey of Machine learning shows it is the collection statistical model base algorithm to make you machine smart in term of prediction and analysis. ML is not a new technology, first introduced in 1950 till now a lot of modern algorithms make their place in ML domain. ML used in different domains as well like Fraud Detection, Data Analysis, and many more.
Data Mining:
Data mining (DM), is the process of finding/Discovering the designing pattern from large amount of data set by using different algorithm including the machine learning, statistical and database, therefore it is also called knowledge discovery in databases (KDD).
In simple word, in this process we are finding those data set which is invisible but inherited from actual data set that is accurate and useful for us after discovering.It is an iterative process of creating a predictive and descriptive model, by uncovering previously unknown trends and pattern in vast amounts of data in order to support decision making. Also, DM applied in the limited area than machine learning because its domain big. Quite often, the data set in Data Mining is massive, complicated, and/or may have special problems (such as there are more variables than observations).
Now it is time to compare them in term of working, performance and importance':
1. It is used for extracting knowledge/information and describing about pattern on it from a big amount of data WHEREAS it is used to predict discrete/continuous value using different statistical and modern algorithm.
2. The concept of Data Mining introduced in 1930 WHEREAS it is introduced in 1950.
3. Its origin based on unstructured data/data set WHEREAS it is not necessary only unstructured data will be use.
4. Generally used to identify the classification and clustering between/among different pattern WHEREAS it is not only used for that purpose also for fraud deduction, spam mail, disease identification etc.
5. It is based/abstracted from Data warehousing that's why another course named Data mining and data warehousing also offered in BS/MS program of CS, WHEREAS it is based on data/machine.
6. The scope or domain of Data Mining is not enough than Machine learning WHEREAS its area is vast in term of usage.
Author, Fahad Hussain MSCS, MCS
Fahad Hussain Tutorial CLICK here.
Voluntary work as a Computer Scientist, to explore the difficulties as simple words!!!
