Will there be any growth in the Machine Learning sector in the future?

By ridhigrg |Email | Oct 7, 2019 | 1221 Views

A quick survey of job sites and portals shows that Machine Learning is one of the biggest emerging opportunities in India. On LinkedIn alone, there are more than 6000 opportunities in the Machine Learning domain. The advent of new technologies such as Artificial Intelligence and Machine Learning has led to large scale innovation and accelerated digital transformation globally.

When looking at career opportunities in the information technology sector, it is worth identifying trends and career paths that have a strong future. Machine learning is certainly one such domain where skilled talent will always be in high demand.

To make the most of a career in ML however, the relevant certification and qualifications are key.

This is because technologies like AI and ML are relatively new in the academic circuit and you need the right resources and training to launch your career in ML. So, while there are many institutes and colleges offering ML as a certificate course or a post-graduate diploma, you need to choose wisely.

Machine Learning makes use of programmed algorithms that receive and review input data to predict outcomes within a pre-decided range. In short, algorithms use a set of 'learning data' that enable computers to learn.

To launch your career in ML, one needs to start with the basics.

1.Start with Statistics :
You do need to understand and apply Statistics well to become a machine learning professional. Some recommended topics are:
  • Data sampling
  • Data structures, variables, and summaries
  • Probability theory
  • Bayesian probabilities
  • Maximum likelihood estimates
  • Hypothesis testing
  • Model selection statistics
  • Gaussian Distribution
  • Estimation statistics
  • Statistics in Model Selection

2. Python or R or preferably both for data analysis:
Machine Learning requires expertise in programming languages for statistical analysis. Two of the most widely used languages are Python and R.

These are both equally popular languages for ML and mastering either one will make it easier to grasp the other.

Some of the topics you must cover in programming:
  • Reading, importing and exporting data
  • Data cleaning
  • Data review
  • Data manipulation
  • Data visualization

3. Applying ML algorithms :
After gaining proficiency in statistics and programming languages, it is recommended to take up an exploratory data analysis project. This will allow you to learn single variable explorations, pair-wise or multivariable explorations and visualization or communication using Tableau.

Next is the application of ML algorithms and

libraries. There are many packages, libraries, and APIs such as Theano, TensorFlow, Scikit-Learn, etc. An ML engineer needs to apply these effectively choosing the right model.

4. System design and software engineering :
Ultimately, the goal of machine learning and artificial intelligence activities is delivering a product or software. Sometimes the project that an ML professional works on is a small component that is part of a bigger ecosystem of products.

A sound understanding of software engineering, system design, and information architecture is important to succeed as an ML engineer.

Machine Learning is certainly one of the most sought-after fields in the tech industry. The time is right to get on the ML and AI bandwagon. A professional course or certification can help you get on board quickly and provide the much-valued skills to become an ML professional.

For working professionals, ML is an exciting field that can augment their career and open up new avenues.

Source: HOB