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Top Career Opportunities after Machine Learning Course

Machine Learning Course

The emerging technologies like Machine Learning and Data Science are sure to remove the lines between digital and physical spheres. Not only a rewarding career but also an exciting job with ample growth opportunities is also guaranteed for the new-age ML engineers and data scientists.

If you are a tech-freak with a background in computer science and want a get-going career, then machine learning is your thing. This futuristic technology is full of learning potential for the ones who love professional challenges.

It is expected that there will be 2.3 million job opportunities available in Machine Learning by 2020. As per U.S. Emerging Jobs Report 2017 published on LinkedIn, the job opportunities in machine learning have grown by 9.8 times in the past 5 years.

The entry-level machine learning engineering can kick-off their career with a handsome package of ₹ 700,000- 1000,000 LPA in India. After gaining the profound knowledge of algorithms and data analysis with a few years of experience, your package will be multiplied.

Skills you must have to become machine learning pro

Computer Science & Programming Knowledge

Machine Learning is a thing of the professionals with a computer science background. Engineers seeking machine learning job opportunities must have adequate knowledge of algorithms (sorting, searching, dynamic programming, optimization, etc.), data structures (multi-dimensional arrays, queues, stacks, graphs, trees, etc.), computability & complexity (P vs. NP, big-O notation, NP-complete problems, approximate algorithms, etc.), and computer architecture (cache, bandwidth, memory, deadlocks, distributed processing, etc.).

Are you accustomed to Machine Learning Libraries & Algorithms?

For a wannabe machine learning engineer, it is mandatory to be accustomed to the standard execution of machine learning algorithms. The ML algorithms are majorly available through APIs/libraries/packages. One should know about the related pros and cons of implementation approaches.

Do you know Statistics & Probability?

Good command over probability and its technical derivatives like Markov Decision Process, Bayes Nets, and Hidden Markov Models is also essential for the machine learning job seekers. Acquaintance with analysis methods is essential for using the validated data for building and validating data.

How much do you know about System Design & Software Engineering?

A rewarding career in Machine Learning is possible if you have a strong background in system design and software engineering. You should possess the knowledge about building the right interfaces for your component. If a candidate holds sound knowledge about the best practices of software engineering, such as requirement analysis, modularity, system design, testing, version control, etc. then he/she become the top pick by the recruiters.

Job Roles in Machine Learning

 Machine Learning jobs in India and other countries primarily encompass Machine Learning Engineer, Data Scientist, Data Architect, Cloud Architects, Data Mining Specialists, Cyber Security Analysts, etc. Let’s go through the in-demand ML positions.

Data Architect/ Data Engineer

The prime role of data engineers is to create and maintain the big data ecosystem of an organization. With a good hand at programming, they must be accustomed to MapReduce, Hadoop, Hive, Cassandra, MySQL, NoSQL, MongoDB, SQL, data programming and streaming. Additionally, your knowledge of Python, R, C++, Ruby, Java, Perl, SPSS, SAS, etc. will also be appreciated.

The major responsibility of data engineers is to develop, build, test, and monitor the scalable data management systems. To design software components and custom analytics applications is also included in the job portfolio of data engineers.

Key Skills: Deep Learning, Python, R, Machine Learning, Data Modeling, Advanced Analytics, Data Collection, Data Mining, Predictive Analytics, etc.

Areas of Responsibilities

  • To define and implement predictive and descriptive modeling.
  • To enable customers in actionable insights.
  • To scale-up analytics outcomes by conceptualizing analytical solutions.
  • To employ machine learning techniques to build and optimize classifiers.
  • To integrate data for analysis by verifying, cleansing, and processing it.
  • To implement ad-hoc analysis and present precise results.

Machine Learning Engineer

Creating flawless and seamless algorithms to interpret the massive data in a meaningful manner is the main job of machine learning engineers. Python, Java, Scala, C++, and JavaScript are the key focus areas for a machine learning engineer. ML engineers should be a team player with a focus on personalization while developing highly-scalable distributed systems. Machine learning engineers should be well-versed at building and implementing ML algorithms/ applications including anomaly detection, clustering, prediction, or classification to undertake the business challenges efficiently.

Key Skills: Cloud ML, Deep Learning, Natural Language Processing, Algorithms, Big Data, Python, Artificial Intelligence, Java, Architectural Design, etc.

Areas of Responsibility

  • To design and implement enterprise-level and complex Machine Learning & Data Learning Applications and systems.
  • To minimize labor in production by implementing automated methods using AI techniques.
  • To build MI pipelines and API services by deploying knowledge of distributed systems.
  • To deliver highly scalable and low latent machine learning solutions for analytical products.
  • To leverage open source libraries and distributed systems to drive and implement solutions.

Data Scientist

A data science job aspirant is assessed on the basis of his/ her expertise in R, SAS, Python, SQL, MatLab, Hive, Pig, and Spark. Usually, the data scientists are expert in Big Data analytics tool and technologies. They employ their coding skills to interpret the humungous & unstructured data as business insights so that the future business decisions can be made easily. Data scientists are pro at cleaning, managing, wrangling, and structuring the big data received through multiple resources.

Key Skills: Deep Learning, Machine Learning, R, Python, Data Mining, Advanced Analytics, Data Modeling, Data Collection, etc.

Areas of Responsibility

  • Should be experienced in data wrangling & feature engineering.
  • Should be familiar with machine learning algorithm development.
  • Should be accustomed to churning out predictive models.
  • Should have adequate knowledge of cloud platforms.
  • Should be familiar with languages like Python (primary), Sklearn/Pandas/Numpy, Deep Learning (Tensorflow, Keras etc.), NLP(StanfordNLP, Open NLP, Gensim, Spacy), etc.

Data Analyst

Hiring managers love the data analysts with knowledge of data storing & retrieval systems, data warehousing & visualization using ETL tools, business intelligence concepts, and Hadoop-based analytics. Data analysts are enthusiasm- driven and usually, they are good at mathematics, statistics, programming, and machine learning. Core areas of their responsibilities are: creating and implementing algorithms, extrapolating data using advanced computer modeling, culling information and identifying risk factors, pruning data, and triaging code problems.

Key Skills: Software Development, Data Visualization, Data Analysis, Data Warehousing, Data Modeling, Data Warehousing, etc.

Areas of Responsibility

  • To have a great understanding of interactive channels (search, online media, website/mobile development, etc.) and direct marketing principles.
  • To automate and advance the current systems by employing the latest process and IT advancements.
  • To provide automation support for converting specifications about business challenges into a sequence of detailed programming instructions.
  • To create, implement and maintain the best business processes by performing in-depth data research and analysis.
  • To involve in Software Development Life Cycle, right from analyzing the requirement and documentation to conducting user acceptance testing.

Machine Learning: Future in India

The future of machine learning in India seems bright. The need for professionals in the areas of AI, Machine Learning, and Deep Learning has been soaring nowadays. If you want to gain the expertise of machine learning engineers, then check your eligibility and get enrolled for online industry-based certification courses.

A programmer, graduate in mathematics or even the simple Bachelor of Computer Applications can go for machine learning course in India. In fact, if you possess a master’s degree in Social Science or Economics, then also you can go for ML online courses. Go for a Machine Learning & AI or Data Science Certification course to nurture your career in the area of machine learning.

An ML Engineer needs to remain abreast of the changing trends in the world of machine learning. Knowledge about the latest tool development, theory and algorithms is also mandatory for a machine learning engineer.

Online forums and communities are the best ways for machine learning engineering to stay connected with the experts and know about the newer trends. The ML engineers should also attend bootcamps and hackathons to brush up their skills.



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