Implementation of machine learning technologies in an organization is becoming a widely accepted concept and approach to encash massive amounts of data. This also implies that data science professionals must upskill themselves with machine learning skills.
The accelerated digitization at organizations and proliferated use of smartphones among the public have created massive amounts of data. Organizations need to get smarter for using data efficiently.
Using cost-effective computing power can help them to derive more insights at a faster rate than ever before. The application of machine learning technology enables them to decipher disparate data faster, arrive at decisions wiser, and plan their efforts logically to gain more returns on investment.
Machine learning is used to achieve higher levels of efficiency. It is a specific subset of Artificial Intelligence that trains a machine how to learn.
In simple words, machine learning makes the computer system robust by learning and solving problems automatically through previous experiences and set rules (algorithms). The application of advanced analytics and machine learning techniques helps data science professionals to predict and optimize business outcomes.
Advancement in machine learning and automation supports the complete data science life cycle from preparing data to building, managing, and monitoring. Here’s how machine algorithms transform data into insights.
The data input can be in the form of JSON, Excel, HTML, Text, image, RDBS, etc. The data is formatted on translational measurements such as numerical data or categorical data.
The data is then imported into a machine learning application programming interface (API). The machine learning APIs have three procedures of code -the preparation code, pre-processing data, and machine learning algorithms.
Before passing the data into ML models, it should be processed depending on the algorithms.
The data gets transformed into useful information as it passes through the API of machine learning.
Industries today are working with large amounts of data and invariably are using machine learning technologies to glean insights from data – mostly in real time. Several industries such as healthcare, construction, financial, retail, transportation, oil and gas, pharmaceutical, food, insurance, airline, mining, and semiconductor industry use machine learning.
A few of the industries using machine learning technologies are briefed here:
Machine learning technology enables the financial service industry to identify insights in data and prevent fraud as well. It helps for identification of investment opportunities, clients with high-risk profiles, and detection of fraudulence.
Machine learning technology enables medical experts to analyze data, identify trends (in case of an epidemic), or red flags (anticipate a disease in the near future). It helps for improved diagnosis and treatment plans.
Retailers are increasingly using machine learning to capture and analyze customers’ shopping patterns, implement a marketing campaign, optimize and merchandise logistics.
Analyzing and identifying patterns and trends is very much important for delivery companies. Machine learning enables them to predict potential problems, make efficient routes, and increase profitability.
It is one of the industries where machine learning is explored extensively. Implementation of machine learning technology enables them to sift precisely through signals/noise in seismic data, analyze large amounts of data, reduce risk during exploration drilling, improve subsurface characterization, optimize, and automate drilling.
As industries realize the power of data, they prefer to hire data science professionals who have gained a deeper set of skills and capabilities in ML and AI along with the fundamentals of data science. Take a look at the necessary data science skills a professional must possess and their practical applications here.
At this juncture, you may be thinking about how can a data science professional or a data scientist can acquire proficiency in all these enlisted skills. It takes time, dedication, and experience. Also, you can learn faster by upskilling yourselves by pursuing commendable data science certifications.
Machine learning algorithms can create better useful results within a shorter period. You can use the time to optimize these algorithms and make them work on new problems. The possibilities for growth as a data science professional are becoming vast.
A career in data science is a team sport rather than merely a job for earning. Be a unicorn in your data science career and find yourself fledged with job prospects. Competition is intense, but the salary compensation is high at the same time. Top up your data science skills with machine learning skills today.
Plan your approach to data science skills and flourish well in your career and also grow your team’s capabilities.