The Thin Line Between Machine Learning and Data Science

Machine learning has recently become a part of our daily lives. These two, along with data science, will have a long-term impact on our lives. Despite their relevance, most people do not have a thorough understanding of these technologies. Even for those who are familiar with artificial intelligence and data science, there is a lack of understanding of how the two are related. Let me discuss in this article the difference and correlation of these terms. 

Machine Learning

Machine learning is a field of AI-based on the development of algorithms that can process data and learn on their own. The entire technique is based on the idea that teaching a computer how to learn is more efficient than programming it to accomplish all of the needed tasks that are part of a broader goal. Machine learning has a wide range of applications, and it’s easy to locate a handful that is getting momentum. The rise of the virtual assistant, such as Amazon Alexa or Apple Siri, is the first. Learning algorithms are used in these systems to fine-tune or tailor the responses to particular user requests. As the system gains a greater understanding of the user’s habits, it will be able to better manage ambiguous requests. Face recognition is another prominent application, in which a still image is fed into a system that recognizes the people depicted within it. Facebook and other social media platforms are capable of analyzing photos and identifying friends in them. Similar algorithms are used to locate and suggest people you might know, as well as jobs you could be a good fit for.

Data Science

Data science is a broad term that encompasses a variety of techniques and approaches. Rather, it’s a catch-all word for a variety of fields. Machine learning, data mining, data analytics, and statistics are all examples of this. It also includes processes such as extracting, converting, and loading (ETL) data into storage repositories, which are all part of working with big data. Data science’s main purpose is to make sense of data. The process of gaining this knowledge is multi-step. This may include the collection and processing of vast volumes of data, depending on the circumstances of a project. If, on the other hand, data loading has already been finished, predictive analytics employing technologies like machine learning algorithms and deep learning neural networks falls neatly within the domain of data science.

Machine Learning and Data Science Interrelation

It’s worth considering machine learning’s significance in the wider picture now that it’s completely integrated into the area of data science. Because data science is multidisciplinary, it employs a variety of technologies that aren’t typically associated with machine learning. Data scientists’ work includes the use of visualization and applied statistics, in addition to pattern recognition and other data mining technologies. The data scientist’s job does not end once the machine learning phase is completed. The results of predictive models will be compared and studied, and the findings will be presented. In addition, the models themselves could be used in the next stage of the exploratory or analytical process. All of this falls under the purview of data science.ls for gathering, cleaning, manipulating, and storing data will be used by a data scientist. These steps are completed before the analytics, regardless of the workflow or technologies utilized. Machine learning techniques can be used to develop predictive models for regression or classification tasks once the data has been fully pre-processed and is ready for examination.

When Deep Learning and Machine Learning Collide

Deep learning is a subset of machine learning but in reality, the phrases are sometimes used interchangeably since they have comparable functions. From a practical sense, the difference is in their capability, which has an impact on their overall contribution to the model. A classic machine learning algorithm relies on user input to guide the learning process. Heuristics can, for example, be programmed to assign a score based on how good a proposed solution is. If the model fails to function as expected, the user must usually alter the appropriate parameters and try again. This change is not necessary with a deep learning system. The algorithms are capable of scoring results and making adjustments on their own.

Conclusion

Machine learning and data science are all popular topics, but many people aren’t sure what they’re talking about. Whereas artificial intelligence encompasses deep learning neural networks and machine learning algorithms, data science is both wider and not fully contained within its purview. In simple words, data science is the process of extracting meaning from data. Because machine learning algorithms can learn from data collection, they are frequently utilized to aid in this search. Deep learning is a branch of machine learning with enhanced capabilities. Deep learning, according to many scientists, has the potential to become the foundation of actual artificial intelligence or strong AI.