As the pace of technological advancements accelerates, we are witnessing an increase in demand for two futuristic fields that offer an exciting and rewarding range of career opportunities: artificial intelligence (AI) and machine learning. If you are seeking to set foot in these revolutionary fields but are unsure where to begin, we have tried to catalogue a road map for you.
Artificial intelligence is a broad field in which machines imitate human thought, behaviour, and functions. It is a robust field with applications in a multitude of industries. A segment of AI is machine learning, where statistical models, algorithms, and programs teach machines how to improvise using new data and experience so they can make decisions and make predictions.
Simply put, it is when computers learn to do things by improving over time without being given specific inputs.
Examples of artificial intelligence and machine learning include photo identification in Google, chatbots, and virtual assistants like Alexa.
Required skills and resources:
- Programming: A strong foundation in languages like Python, R, and Java is essential to pursue a career in machine learning. However, the scope of Python appears to be higher than the rest.
- Understanding Data Structure: Arrays, lists, stacks, and queues are data structures that will help you store and access data efficiently.
- Mathematics: Artificial Intelligence career paths typically require a sound understanding of mathematical principles for computation; it provides the foundation for developing an algorithm that powers these technologies. Proficient knowledge of linear algebra, standard deviation, calculus, probability theory, and statistics is necessary to apply various AI models.
- Software Engineering: You should be up to date with software engineering principles like object-oriented programming, version control, and software testing.
- Machine Learning Algorithms: Familiarization with algorithms and instructions such as linear regression for modelling the relationships between variables, logistic regression to maximize probability by assigning class values to new observations, support vector machines, clustering to identify subcategories in data, etc.
For beginners to establish a career:
- Build your foundation: Pay close attention to the basics of coding, calculus, statistics, other mathematical concepts, etc., and master them thoroughly. There are plenty of online resources and reputed institutions such as CMR IT to provide you with a strong foundation.
- Strengthen your skills: Once you know the basics like the back of your hand, focus on building advanced skills like machine learning algorithms and software engineering that require repetitively revisiting the basics and consistent practice. Since the field is innovative, you will need to keep upskilling.
- Get Certified: As a booming industry, the job market for artificial intelligence and machine learning is highly competitive; therefore, to show your expertise, learn from reputed institutions and attain certifications that help you stand out to potential employers.
- Get hands-on exposure: Despite a certification and theoretical knowledge of the subject, practical experience goes a long way. Taking on projects and working on real-world problems is a great way to secure a job and expand your knowledge.
- Expand your reach: After acquiring relevant skills, certifications, and experience in artificial intelligence and machine learning, you can start applying for jobs, reaching out to companies with your portfolio, networking to avail yourself of professional opportunities, or even just staying up-to-date in the field. Metagroups and LinkedIn connections are great ways to approach people and network with like-minded professionals.