Machine Learning — Data Analysis
Series :1
Introduction: https://medium.com/@neel4r/machine-learning-data-analysis-830af7666b0d
Basic Steps involved in process of ML
The steps which are followed in process of AIML can be correlated with base workflow (cooking, painting, playing musical instrument, etc) which are done with continous feedback and improving the effectiveness of the output.
The process can be split into three stages:
- Data Design and Data Analytics
- Artifical Intelligence
- Machine Learning Algorithm
The big picture of the process is shown the below picture.
- Data Design and Data Analytics
Data Design
This basic and most important process which lays the foundation for the whole AIML process.
Collection of the data from the source in the basic table formate with all required parameter which are in process. For a example, we can consider a health care monitoring system, where doctor need to monitor some vitals for analysis the health condition of the patient. In this case we need have information in each respective columns.
Data Analytics
Before we perform any data analytics on the data, we need to clean data. As part data clean we need remove any null data present in dataset and fix typo issue if any thing present. For analysis the data we have lots of tools among them excel, Tablaeu, Power BI tools are used widely for the data analytics. Data Analytics also done by the Python and R programming. Selection of the tool depends on the indivdual or organization on their preference.
As part of analytics process one should have understing how to visualize the data which help in deriving the insight and help in understanding the pattern to helps to desicions. Data visualization is only for human understanding only not for the machine.
2. Artifical Intelligence
Artifical Intelligence algorithims understand the effect of parameters over the targeted variable. Which is similar to the finding the correlation of each parameter on another and on targeted parameter. Their numerous methods are present in world of AI this basic once. We try to explore on upcomming series.
3. Machine Learning Alogrithms
Machine Learning Alogrithms are most important for predicting future either regression or classification problem. ML has various kinds on of each have their own pro and cons. It upto the programer to pick which once to why to pick by observing the key parameters like Accuracy, Precision, F1-score and Kappa for the Classification kind of problem and similar RMSE for the Regression kind. How to approach this kind of problem will discussed in upcomming series with woking demo.
Note: Refered resources are present in reference links.
Reference