Machine learning coding is one of the most important aspects taught as part of important machine learning topics. In most courses, Machine learning coding and data science programming are used interchangeably. While there is nothing wrong with doing so, experts believe that using Machine coding as a machine learning topic can derail the future aspirations of many developers who are pursuing coding specifically for Artificial Intelligence applications.

In many ways, data science and machine learning coding are very much similar—yet it’s the differences that we should focus on to better understand the scope of using programming for AI ML models.

Let’s understand the key differences between different types of Machine Learning coding models.

3 Different Skills you need to become an ML coder

Machine learning engineers are required to master three distinct machine learning topics. These skills are:

Data Science skills

Data science skills are fundamental to growing with an ML career.

You have to embrace and acquire the ability to understand the nature and scope of data management and data storage trends and evaluate how different events influence data behavior. These entail learning and using data science fundamentals such as hypothesis testing, regression analysis, probability, and conditional statistics modeling.

Software Development skills

From the DevOps community, we have learned the finest nuances of the software development lifecycle and its impact on Machine Learning modeling. Many ML coding projects revolve around basic software development, testing, and experimentation concepts.

It is the key to understanding the relationship between using software for various ML development processes and automating a part of operations to software engineering techniques.

If you are familiar with the computer science fundamentals and use these to create machine learning pipelines, you would be able to build a near perfect ML networking architecture in the shortest span of time with minimal effort.

Analytics skills

Analytics could mean different things for a data science professional as it would to a machine learning developer. Machine Learning analytics is a fast moving ecosystem where different AI ML topics are being developed to solve numerous challenges associated with emerging techniques such as Computer Vision, Natural Language Processing / Programming (NLP), Auto ML, and so on.

One area where analytics with machine learning models is widely used is that related to business intelligence where analysts are using advanced research capabilities with web analytics, customer data management, and advanced predictive intelligence to improve the scope of other data science applications, such as sales forecasting, marketing automation, email marketing, and advertising data analysis, and so on.

Coding Benchmarks in Machine Learning Development

Now that we have covered the three supersets in machine learning development, let’s understand how ML coding differs from data science programming.

Top ML Programming Languages

The benchmarks in ML coding depend on the choice of programming language. Each programming language has its own set of coding practices and benchmarks that impart a level of sophistication at each stage of testing and application. ML developers often take it easy to code programming languages against traditional languages such as C/ C++ and Java. That explains why we see a majority of ML practices embracing Python and R to code for advanced applications such as Semantic Analytics, Text intelligence, and Conversational AI.

Testing for Purpose and not History

Machine learning developers are really smart at consolidating data sets and improvising on a basic ML platform to create something totally new. For example, an Uber ML model that is used to track the passenger density of cab hailing requests can be used more or less to identify the best route to reach a destination. These works are based on the IP addresses of the mobile phone users and build upon telemetry data and maps. Similar ideas are implemented whilst comparing food delivery apps with e-commerce booking apps or travel planner apps. In general terms, ML coders always hunt ideas to solve current problems rather than testing models for old case studies. That’s a key difference when you compare a data analysts’ job with an ML developer’s job in data science projects.

As machine learning topics continue to throw up new ideas and challenges, we should expect further branching in data science.

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