Starting Your Career in Machine Learning: What Are Your Options?

The field of Machine Learning and Artificial Intelligence are grooming and are currently in the hype. Many individuals are willing to understand and learn more about this technology. So, it might not be a bad decision to choose the career path in Machine Learning and Artificial Intelligence. In today’s market, Machine learning has been increasing and is most likely to increase even in the future.

Career in Machine Learning
As the demand for Machine Learning is increasing, many individuals are willing to learn more about the field of Machine Learning. However, some of them are unaware of the pathway towards learning this technology to have a successful career in Machine Learning. 
Therefore, it is necessary to understand the requirements that a learner should consider building a career in Machine Learning to have a clear vision and goal. Here, with a clear goal and consistent effort, a person can surely succeed in the field of Machine Learning. With that being said, let’s have a look at various Machine Learning Career Paths and their requirements.

Machine Learning Career Paths

            First, let’s have a look into the various career paths of Machine Learning.
Machine Learning Career Paths


1.AI Engineer

            AI Engineer plays a vital role in the machine learning career path. So, to kick-start your career as an AI Engineer, there is a certain skill set that the individuals should acquire before applying for the role. For this, it is preferred to have skills in Deep Learning, Machine Learning, and various programming languages.
     Furthermore, individuals should be able to create a specific computer and prepare documentation, including various flowcharts, layouts, diagrams, statistical charts, etc. Also, various testing should be carried out for obtaining optimum value delivery and customer satisfaction.

Skills required:

       Should be proficient in multiple programming languages such as Python, Java, R, etc.
       An individual should have a good knowledge of data structures and data architecture.
       Should have a good understanding of operating systems, computer architecture and distributed processing.
       Should have excellent knowledge about ML concepts, algorithms, systems, and tools.
       Should be familiar with software testing and debugging.
       An individual should have sound knowledge of data analytics. 

2.Machine Learning Engineer

            Machine Learning Engineer is one of the promising career paths in the field of Data Science. As a Machine Learning Engineer, one is familiar with working with real-time data and should be able to develop various algorithms. Machine Learning engineers should know about supervised and unsupervised learning, regression, classification, and time series modeling to develop the algorithm. 
        Furthermore, it is the responsibility of Machine Learning Engineer to develop self-learning Machine Learning application after analyzing the statistical data. Also, Machine Learning engineer can conduct various experiments using various programming languages such as Python, Java, R, etc. for obtaining better results. 

Skills required

       Strong foundation knowledge of Statistics and Mathematics.
       Should have fundamental knowledge in programming.
       Experience in data modeling and evaluation is required.
       Should be familiar with software architecture and system design.
       Individuals should know data structures and various Machine learning algorithms.

3.Data Scientist

          A data scientist is one of the most preferred career paths in the field of Machine Learning. The individuals who prefer the career path of data scientist should be proficient in the technologies like Big Data, AI, Deep Learning, etc. Furthermore, Data scientists should also have hands-on exposure on various programming languages such as R, Python, Perl, etc. 
       The main goal of a data scientist to gain valuable insights from the large datasets which can be converted into business value. Therefore, data scientist plays a vital role in the growth of any organization.  

Skills required

       Experience in data mining and various Big Data Platforms (Hive, Spark, Pig)
       Proficient in programming languages such as R, Perl, Java, Python, etc.
       Should have proper technical skills and non-technical skills.
       Should be familiar with various analytical tools.
       An individual should be excellent while working with various unstructured data.
       Should have communication skill and have a clear understanding of business.

4.Natural Language Processing (NLP) Scientist

The scope in the field of Natural language processing is also increasing. The main aim of Natural Language Processing is to make the machine understand the natural human language.  Hence, the key role of the NLP Scientist is to guide or teach a machine how to understand human languages with the help of various syntax, spelling, and grammar of at least one language.
 Therefore, NLP Scientist is responsible for developing machines that learn patterns speech of human language and can translate the given the word in other programming languages. 

Skills required

       Should be familiar with Machine Learning skills
       Should be proficient in syntax, spelling, and the grammar of one language or more languages.

Tips to consider before applying for a Machine Learning job

       After getting to understand various career opportunities in Machine Learning and their roles and responsibilities, one should decide which job best suit their preferences and apply for a Machine Learning job. 
      However, some factors should be considered before applying for the job of Machine learning. Some of them are mentioned as follows.  

1.Continuous learning and Practice regularly

There is a popular saying – “Practice makes a man perfect”. Similar to that, when an individual acquires knowledge to write code and build a machine learning model, they should practice it more often. 
Also, the experience and practices while writing codes and models can be showcased as personal projects to the potential employer. Therefore, an individual should try their best to implement and practice the acquired knowledge. 

2.Take part in competitions

         Demonstrating practically in various relatable competitions not only enhance knowledge but also boost confidence and will to perform better among the learners. When an individual demonstrates his/her knowledge to other personnel, they can learn better ideas and see if there is any room for improvement. 
            Furthermore, they go through every aspect of the project before demonstrating the project to others which makes their base of Machine Learning stronger. So, it would be very beneficial for learners to take part in Hackathons and other competitions. This not only enhances the skills of individuals but also helps to maintain a strong resume. 

3.Build a strong resume

           Resume plays a vital role in getting any job as it is the first impression of a job seeker to the employers. So, an individual should try their best to make their resume stand out among others to get a job that they think they deserve.
      Hence, the resume should comprise of programming, statistics, and data modeling. Additionally, it would be very effective if the individuals maintain their portfolio or GitHub page.

4.Carry out Preparation before the interview

          An interview is a final stage that determines whether a person gets a job or not. So, first of all, it’s better if an individual can identify the problem in the company. For that, it is sometimes necessary to research every small and big detail about their potential job place before their interview so that they can relate to the job role and act accordingly. 
            Hence, if an individual can present the solution to the problem present in the company, it can add a huge advantage in the interview session. However, this might not be possible in every circumstance. So, in such a case, an individual should understand the background of the company.
        It is always a boon to acquire new knowledge and skills. As the market of Machine Learning is continuously growing, it can be fruitful to keep your hands-on Machine Learning. Sometimes, acquiring knowledge about a new skill set can be time-consuming but spending time to acquire knowledge about Machine Learning is most likely to have a positive outcome. 
   Hence, it is better to learn more about Machine Learning and get prepared for tomorrow’s market.

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