Applications of Data Science and Machine learning include deriving valuable insights from the huge amount of data given, which helps the organization to make important decisions. Machine learning is developing computer systems that learn to adapt by using algorithms and statistical models to analyze and draw inferences from patterns in data.
Earlier, as the data was limited, it was analyzed by a team of statisticians, analysts, and mathematicians. But as the companies became more and more digital, the importance of data and machine learning became more crucial. Internet and cloud computing became the foundation of the companies. A considerable amount of data is generated daily. With the rise of powerful search engines like Google and YouTube, it became necessary for companies to handle their data more carefully.
Applications:
Machine learning and data science are widely used in all the major fields. Machine learning and data analysis are used in the healthcare field to detect cancer cells in the patient’s body. Companies like Zomato, Swiggy, Spotify, and YouTube use the recommendation system algorithm of Machine learning to content based on their previous orders and likes.
The life-cycle of a Data Science cycle is divided into six major steps.
- Data Acquisition
In this process, data is collected and collected by considering the type of problem to be solved.
- Data Pre-Processing
This process involves arranging data in a structured manner for easy use.
- Explanatory Data Analysis
This process is one of the most important steps where the summary of the data collected by statistics and maths is done. Programming plays a very crucial role in this step. A data scientist spends almost 75-80% of his time on this process to understand the data science trends in the market.
- Model Building
After the explanatory data analysis step comes to the important step of model building, in this process, appropriate methods are chosen to build a model. This step is performed with the help of machine learning. Algorithms like classification, regression, or clustering are selected. Depending upon the problem to be solved, a selection of algorithms is made.
- Evaluation Of Model
In this process, the efficiency of the model is checked.
- Development of a model
Model development is done to serve all the future data for making further predictions.
Conclusion:
Machine learning and Data Science are in trend in the digital world. Kick start your data science and Machine learning journey with the online data science course offered by Analogicx
