The primitive Business Intelligence (BI) methodology has its primary focus on data sourcing from disparate source systems and data augmentation in a data lake or data warehouse. This respiratory of data acts as the primary source purpose like reporting, data marts, and data mining. All these data analysis forms require the end user to apply analytical thinking for result interpretation.
Machine Learning, being an advanced analysis forms where the model learns from the model of data fed and for predicting analysis through derives intelligence. This analysis majorly depends on the model of machine learning to develop the process. It is the combination of data transformation/modeling, model training, model improvisation, and model testing and data analysis.
Professionals often think that their database experience covers exploratory skills of data analysis. The professionals of database professionals are fluent in data analysis which is more of a query logic/ database model assessment. The study of exploratory data that is involved in machine learning systems is nature wise statistical and often named as data science.
ML has deep roots in statistics that are required to create a solid foundation for data science basics for exploratory data analysis. We can divide statistics into two broad categories- inferential and descriptive and is widely used in the model development of machine learning.
SQL Server hosted data provides the benefits of a predefined schema and T-SQL constructs. SSIS and other ETL tools provide the benefits of data transformation at a broader scale and faster pace. Assuming data is concisely structured and treated for errors during data quality/ capture, exploratory data analysis can be applied over this data, the fundamental step in machine learning model development. Model training, model development and model training follows this analysis.
What is Machine Learning and reason to learn?
When we train a machine to learn from a given dataset, we can use these items for distinct purposes like prediction, classification, and others; we call this concept as Machine Learning. One more point to learn is that a machine not only means a physical device. For easy understanding, it can be perceived as a program or a data model.
Some key points and definitions related to Machine Learning are mentioned below:
Machine Learning is concerned with automatic concerned programs to improve their performance through expertise.
Machine Learning is one of the types of AI provides the computer devices with the learning ability without any explicit programming.
ML primarily focuses on computer program development that can change with new data exposition.
The process of ML is comparable to data processing. Both systems search through information to appear for patterns. However, rather than extracting information for human comprehension as just in case of knowledge mining, ML uses that information to discover trends in data and alter program actions consequently.
Some of the applications mentioned below will provide the best answer for the question, why learn ML.
Machine Learning Applications
Machine Learning Types
The types of ML learning can be found in distinct reference materials. Usually, the process of ML classifies into three categories as Supervised, Unsupervised and Reinforcement Learning.
Supervised ML: This form of ML learns from unlabeled knowledge and takes actions. For instance, think about a dataset containing attributes of all the homes in a given country or state or town. Also, even if it is, prediction intends to predict the price of a given home based on attributes and not which house the attributes belong.
Unsupervised ML: This form of ML learns style unlabeled data and then takes actions. The best example is “consider a dataset with attributes of all houses in a particular country or state or city.
Reinforcement Learning: In this form of ML, the learning is possible based on the rewards according to the depending system upon the actions performed by the model. This is the most advanced machine learning form applies to AI-based systems like robotics, neural networks, and recommendation engines.
Machine Learning Support in Microsoft Technology Stack
ML Support in Microsoft Technology Stack
Microsoft acquired R in 2016 enabling a vision of Microsoft data platforms on-premises, hybrid environments and on Microsoft Azure. Microsoft post-acquisition integrated R with SQL Server, Azure, PowerBI, and Cortana Analytics. Additionally, Revolution R open has been renamed to Microsoft R Open and Revolution R Enterprise to SQL Server R Services and Microsoft R Server.
R Services from SQL Server/ SQL Server ML Services installs an open source R distribution as well as packages provided by Microsoft that support distributed and parallel processing. This architecture is specially designed to enable external scripts using R run in a separate process from SQL Server. R services integrate the R language with SQL Server and help to perform analytics close to the data and eliminate the security risks and costs that are associated with data movement.
The methodology of traditional data analytics relies on transforming and transporting the data from OLTP databases> Data Warehouses> Data Marts using Power shell administration, SSAS for in-memory analytics and multi-dimensional, and reporting SSRS. Manipulation of data using set-based operations and numerical algebra has been the perfect solution with T-SQL on data stored in OLTP databases. Using T-SQL and R extends the data science power, machine learning, and statistical computing and other advanced predictive analysis capabilities to OLTP systems.
In this tutorial, we'll be acting active exercises exploitation R and T-SQL for exploratory information analysis and machine learning. It's assumed that you just have already put in SQL Server 2017, Machine Learning Services still as R. just in case you've got not, you'll learn the way to that here.
How Statistics are used in Machine Learning
ML has deep roots in Statistics and Mathematics. Here are some distinct phases of an ML model development with their order.
In the process of ML model development, the initial step is data exploration. Here the investigation does not mean data querying form distinct sources using complex functions, queries or joins.
The exploration intent is assessing the data balance from a standard point to develop a model of ML. If the data is not balanced correctly, it requires both transformations as well as standardization.
Upon identifying the attributes of inputs, an ML model is developed and trained with a significant data portion. The remaining data tests the accuracy of the model’s prediction. Improvising the prediction accuracy of any model is an iterative process until it reaches a level of satisfactory convenience.
Branches of Statistics
Generally, statistics are categorized into two branches at the best level as Descriptive and Inferential.
Firstly, let’s understand about descriptive statistics that explains organization’s data and summarizes it with a representative sample. Its significant parts include Central Tendency Measures, Variability Measures, and Correlation. Quantitative analysis designs this particular branch.
Coming to the inferential statistics, it interprets and determines data as well as statistical significance thus concludes an unknown broader dataset from a sample one. Its foundation lies in the theory of Hypothesis Testing and Central Limit Theorem.
According to inferential statistics, the algorithms number deals with a particular predictive analysis types problems. ML models use these algorithms that mean it requires a detailed understanding of the algorithm before applying.
Studying Statistics of ML
Any ML algorithms explanation starts with statistics. These statistics are usually at a higher level as describing it from the lowest level requiring a separate book itself for each algorithm but do not have the appropriate statistical background to learn these concepts.
Without proper statistics foundation, any tutorial on ML would look like a mathematics class. Therefore, the question is learning statistics without touching the breakdown point where you give-up ML or lose interest due to learning struggle more and more about statistics.
The learning approach is distinct for distinct persons based on their likes and dislikes. One of the following ways is a top-down approach to identify the best starting point. It is recommended to consider any of the statistics topics.
So, in this way you can deduce the point where you have the appropriate background to understand the most fundamental topics and slowly build-up until you reach the statistical terms that are used in ML algorithms.
Some inferences are faster and easier to make with the help of graphical analysis instead of looking at distinct numbers. There are different varieties of statistical visualizations based on the analysis types and variable categories. Some among them are quite fundamental and are almost used in every kind of analysis as a beginning point. The most commonly used visualizations for graphical exploratory study are :
Now let’s assume that you are entirely new to the ML discipline, we started this discussing some basic terms, concepts and ML theory. We have a glance at the components of SQL Server 2017 which supports deep roots in statistics and mathematics. We came across some basic statistics terms, fundamentals and ML learning statistics.
Having a strong statistics foundation, theoretical ML knowledge learning and implementation of R knowledge, we came across that how about the data spread and about the shape of learning distinct statistics that are extracted using T-SQL and R. We have also learned about how to do this graphically by using different statistical visualizations.