How to Start a Career in Artificial Intelligence and Machine Learning - A Complete Roadmap For 2023
With millions of data generated daily, maintaining, monitoring, and managing them is an uphill battle. Today, most businesses are leveraging Artificial Intelligence and Machine Learning technologies to simplify their workflows and strengthen their data capabilities. Using these technologies, businesses aim to streamline their work process, eliminate complexities and manage data effortlessly.
In our introductory guide, we will walk you through the step-by-step guide to help you get started with Al and ML. Prerequisites, knowledge, and skills required for these designations will be discussed in detail. Additionally, we will share tips and strategies to jump-start your career in Machine Learning and Artificial Intelligence.
Let's Start!
AI and ML: An Overview
Both AI and ML are interrelated. The ideal attribute of Artificial Intelligence is the capacity to reason and execute actions with the highest probability of reaching a specific objective. However, Machine Learning states that computers can automatically learn and adapt to new data without human assistance. This autonomous learning is enabled by absorbing vast quantities of unstructured data, including text, photos, and video.
What is Artificial Intelligence?
Artificial Intelligence replicates human intelligence in machines programmed to replicate human thought and behaviour. The term applies to any computer demonstrating human-like characteristics such as learning and problem-solving.
AI is founded on the premise that human intelligence may be characterised so that a machine can imitate it and carry out tasks ranging from the simplest to the most difficult. It aims to simulate human cognitive processes and let the system do human-like work.
Prerequisites for Artificial Intelligence
A graduate degree, preferably in computer science, is an excellent way to qualify for an AI job. However, a degree is not enough to get a job in this designation; skill set matters. Here are a few prerequisites for Machine Learning that one must know to land an entry-level job for this designation.
1. Programming Knowledge
Knowing how to write programs is a prime consideration when diving into an AI job profile. Programming languages such as R, Python, Java, and C++ should be mastered. Give special attention to concepts like data sets and classes. Regardless of your chosen programming language, you must emphasise building the basics and concepts in depth.
2. Calculus, Linear Algebra, and Statistics
A sound understanding of statistics is necessary to understand how programs and Machine Learning operate. Basic statistical concepts like Gaussian distributions, median, and variance is necessary. Also, individuals aspiring for AI designation must be able to forecast reports using probabilities and Naive Bayes model. Moreover, a strong foundation in calculus, integrals, and derivatives, to name a few, are required.
3. Integrative Language Processing
Computers don't understand English or any other language spoken by humans. Thanks to NLP for allowing computers to comprehend and analyse human languages. It brings computers closer to human-level language comprehension, a prerequisite for an AI professional. It is a no-brainer why an AI professional must have a firm grasp of NLP. It is expected to have a sound understanding of libraries like NLTK and Gensim, to name a few. In addition, a sound understanding of sentimental analysis, summaries, and word2vec. Techniques are essential for an AI designation.
4. Neural Network Structures
When jobs become too complex for people to code, networking works. Neural networks compromising artificial neurons, similar to neurons in our body, are proven to be the most effective method for addressing complicated issues. Speech recognition, image processing, and translation, to name a few, are easily handled using network structures. So, anyone aspiring to jump-start their career in AI must get their networking basics clear.
A Career in Artificial Intelligence
A career in Artificial Intelligence is an excellent pick in this continuing technological revolution. AI, the driving force behind computer vision, speech analysis, and natural language processing, influences industry and society in various ways – and will continue to do so for the foreseeable future.
Why is Artificial Intelligence a Good Career Path?
Before we answer why is Artificial Intelligence a good career, let’s have a glance at the last few years' records. The Bureau of Labor Statistics predicts a 31.4% rise in jobs for data scientists and statistical science experts, essential to Artificial Intelligence, between 2010 and 2030. Forbes estimates that Machine Learning occupations will be worth $31 billion by 2024, an increase of 40 percent over the next six years.
AI provides the opportunity to work in diverse industries and technologies. IT, transport, banking, finance, and all the major industries use AI to address their business challenges and solve complexities. Transportation leverages AI to automate self-driving vehicle cars, businesses use AI to evaluate performance metrics, and manufacturing sectors use the technology in assembling parts. Regardless of the domain of your interest, AI fits everywhere.
What is Machine Learning?
Machine Learning comes under the branch of artificial intelligence that enables systems to leverage data and replicate intelligent human behaviour. The primary aim of ML is to make applications super accurate in predicting outcomes without needing to code explicitly.
Prerequisites for Machine Learning
A master's degree is a way to demonstrate your talents, but your practical knowledge and abilities will ultimately help you construct a project or pursue a job in Machine Learning. However, it is not mandatory; the right skill set is a must. Here are some of the prerequisites for Artificial Intelligence:
Mathematical Skills
Understanding mathematics is an essential prerequisite to getting started with Machine Learning. Here are the major areas of mathematics that are mostly used in Ml jobs.
- Statistics
- Linear Algebra
- Probability
Let's understand the importance of each in detail:
1. Statistics
Both Machine Learning and statistics are inter-connected. In fact, the majority of ML concepts come from statistics or are dependent on it. Therefore, an ML enthusiast must have a strong foundation in statistics to solve modern-day problems. For beginners, some of the basic statistical concepts like logistic Regression, distributions, and standard deviation, to name a few, add a great help to these job descriptions.
2. Linear Algebra
Linear algebra studies vectors and rules for manipulating them, matrices, and linear transformation. It is an essential Machine Learning component and enables ML algorithms to operate on many multidimensional datasets.
Although linear algebra is one of the essential prerequisites for Machine Learning, beginners don't need an in-depth understanding of the subject; a fundamental grasp of the concept is required to begin.
3. Probability
Probability assesses the unpredictability of an event with a good level of accuracy. The greater the likelihood of an occurrence, the more likely it will occur. Probability is used in ML to produce predictions using inadequate information. In conventional programming, issues are deterministic, and output is not impacted by uncertainty. In addition, probability assists with hypothesis testing and distributions, such as the Gaussian distribution and the Probability density function.
4. Calculus
Calculus is essential for ML. Beginners don't need an in-depth understanding of calculus; even a basic idea of it is sufficient. You do not need to compute complex derivatives in calculus manually; instead, you must comprehend how differentiation occurs and how it is applied in vector calculus.
In addition to being utilised for algorithm training, multivariate calculus is also employed for gradient descent. Directional Gradient is an essential element in multivariate calculus: derivatives, divergence, curvature, Laplacian, and Directional Gradient.
Programming Languages
In addition to mathematical ideas, Machine Learning requires proficiency in a programming language and the ability to write code.
1. Python
Python is superior to other programming languages because it is easy to learn and provides excellent employment prospects for programmers and data scientists.
In addition to the adaptability of programming languages, Python provides good community support and a vast library collection. As a platform-independent programming language, it offers a comprehensive framework for Deep Learning and Machine Learning. Python is also a highly portable language because it can be used on Linux, Mac OS, Windows, and UNIX.
2. R
Like Python programming, R is a popularly used language for statistical analysis. It is a functional programming language for Machine Learning due to its inherent functional programming, object-oriented design, and vectorial processing. It may not be the ideal language for Machine Learning, but its exceptional performance with large numbers.
3. Java
Java is the most popular programming language utilised by developers and programmers worldwide. JVM enables Java to be readily implemented on numerous platforms (Java Virtual Machine).
In addition to the programming mentioned above, there are other more used in Machine Learning, including C, C++, MATLAB, JavaScript, etc. Python and R are the two most popular programming languages for Machine Learning due to their numerous advantages and extensive libraries. However, other general-purpose languages, such as Java, C, and C++, can also be utilised; however, proficiency in these languages is required. However, selecting the best languages might be difficult for novices.
A Career in Machine Learning
The career scope in Machine Learning is limitless. Its data examining and forecasting capabilities make ML popular for modern-day business problems. ML applications are everywhere, and the demand for professionals with problem-solving skills will continue to grow.
Why is Machine Learning a Good Career Path?
Machine Learning (ML) offers a wealth of high-paying career opportunities in the business world, such as Systems Analyst, Data Scientist, NLP Scientist, Business Analytics Developer, and Human-Centred Machine Learning Designer. If you possess skills in Machine Learning, you can expect attractive salaries and perks. This makes a career in Machine Learning highly desirable.
The demand for individuals with these skills far exceeds the supply, leading to intense competition among employers. There have been reports of tech giants engaging in bidding wars to secure the industry's top AI talent.
Additionally, these ML skills are increasingly valuable in the video game industry, where they are used to enhance game design, create more realistic AI opponents, and improve user experience, further expanding the range of lucrative career options for ML professionals.
How to Start a Career in AI & ML for Beginners
There is no one-size-fits-all strategy to land a job as a qualified ML and AI professional. Due to the level of complexity involved in the job, the expectations from these job roles are comparatively higher than other IT profiles. However, there are proven strategies that a beginner can follow to jump-start their career in AI and ML.
1. Built your Foundation
No matter how fancy the job title may look, mastering the fundamentals is the game-changer. Dive deep into the basics and focus on building concepts that will help you in your ML/AL job roles. Whether it's statistics, probability, calculus, or mere coding, it is essential to have a sound understanding of the subject. A detailed insight into these topics will help you address and solve complex real-world problems.
2. Strengthen your Skills
Strengthen your problematic areas and explore more on the topics that you are falling behind. Evaluate yourself and know where you stand. Read blogs0, and watch ML/AI learning materials to improve your knowledge and empower your existing skills. Upskilling, for instance, is one of the most innovative ways to magnify your knowledge and get the course certification. This will increase your chance of being shortlisted for jobs compared to folks without certificates.
3. Get Certified
Certification plays an essential role in getting an AI or ML job. Not just does it verify your qualifications, but it also adds credibility to your profile. Also, your chances of getting the recruiter's attention are significantly higher than those without certifications.
However, picking the most suitable course is strenuous because of the many options available in the market. No worries! With Board Infinity, we have the one-fits-all-in Artificial and Machine Learning Course with Placement to help you get certified and make you job-ready.
4. Get Hands-On Exposure
Leaning turns out to be most effective when put into practice. Besides theoretical knowledge and course certification, focus on getting hands-on experience. Start building a small project and try to implement what you have studied in theory. The projects will help you catch up on the topics quickly and give a sense of understanding of the nature of AI/ML work. Initially, a mini-project is an excellent way to get started.
5. Expand your Reach
Now that you have learned the necessary skill sets and gained hands-on experience, it's time to get job-ready. Networking is an essential consideration when finding an AL or ML job. Follow and get in touch with professionals in this field. Creating a LinkedIn profile is a great way to reach out to experts and connect. Be ready to work as an intern and never miss an opportunity to learn.
Conclusion
Technology is advancing periodically, and job-seekers are leveraging training for new-age tech positions. Many online training programs and institutes offer AI certification, AI engineer certification, Machine Learning certification, and Artificial and Machine Learning courses with Placement. The majority of AI certification programs have been well-designed by industry professionals. The program provides a lens for validating the applicant's technical competence. It is a bridge between an individual lacking all AI skills and the current industry standards.