Machine learning stands poised as the cornerstone of innovation and expansion in India\u2019s future. The World Economic Forum predicts a surge of 22%\u00a0 in the Indian job market, with AI, ML, and data-related jobs spearheading this growth. Therefore, it\u2019s important to prepare India\u2019s youth with the skills necessary to capitalise on the upcoming opportunities. While there\u2019s no straight road to navigate the machine learning landscape, here, we\u2019ll explore a pathway best suited for a sustained growth and fulfilment in this field.<\/p>\n
1.Foundational Knowledge<\/p>\n
The first step entering this field is to understand the fundamentals of mathematics and statistics. One has to have a clear understanding of linear algebra, calculus, probability, and statistics. This is followed by gaining knowledge about programming languages like Python and libraries like NumPy, Pandas, and Matplotlib for data manipulation and visualisation.<\/p>\n
2.Specialised Learning<\/p>\n
One should acquire an understanding of the core concepts of Machine Learning such as supervised, unsupervised, and reinforcement learning. There are various (Massive Open Online Courses) MOOCs available online that can introduce a keen learner to these topics. One should also prioritise learning data cleaning, wrangling, and exploratory data analysis (EDA) techniques. Followed by hands-on experience with ML frameworks such as TensorFlow or PyTorch.<\/p>\n
3.Real world applications<\/p>\n
The focus now should be on gaining practical knowledge on the chosen path. This can only be done by participating in live projects and competitions to finally build a portfolio. One can even apply for various internships to gain practical knowledge. Aspirants should upload their projects on Github for better visibility.<\/p>\n
4.Advanced education<\/p>\n
Once all the foundational work is done one needs to choose a specialisation where he\/she can focus and pursue their career ahead. Their focus should be on becoming experts in at least one the following ML specialisations such as deep learning, computer vision, or natural language processing. This can be done by pursuing a Masters or a PHD in their chosen field. This step also opens up avenues in research should the candidate choose to pursue.<\/p>\n
5.Networking<\/p>\n
As mentioned in Step3 internships with a PPO are a great way to get one\u2019s foot in the door of the AI industry. Having a github profile with valid projects does help indeed. One can utilise platforms such as Linkedin or attend local meet ups to grow their network and look for growth opportunities.\u00a0\u00a0<\/p>\n
6.Career advancement<\/p>\n
One one has acquired an entry level job in their chosen field of ML they can choose to lead projects in order to rise in the corporate hierarchy. A candidate starting from a position of a machine learning engineer, data scientist, or research assistant can get promoted to mid level positions like Machine Learning Engineer, Data Scientist, or Applied Scientist. They can then aspire to achieve senior level roles such as Principal Machine Learning Engineer or Chief Data Scientist.<\/p>\n
7.Continuous Learning<\/p>\n
Even after one achieves the senior most post the field of ML is ever evolving and one must continuously invest in learning and upskilling in order to be relevant in the industry for a longer duration.<\/p>\n
These are the 7 essential steps for a newcomer to follow and build a career in ML. These are several benefits to working in this field such as high demand, diverse applications, working with latest technology, high job satisfaction and high salaries. All these attributes combine to give a fulfilling career to anyone planning to build a career in Machine Learning.<\/p>\n","protected":false},"excerpt":{"rendered":"