According to a report by the global research and advisory firm, Gartner, Artificial Intelligence will create 2.3 million jobs by 2020. The report further maintains that starting from 2020, various sectors, including healthcare, education, and the public sector will witness a rapidly escalating job demand in AI and related technologies, that includes Machine Learning.
While the ever-increasing adoption of AI and ML across different industries is generating more and more jobs with every passing year, the real challenge lies in finding talented professionals in these fields of Data Science.
The primary reason for this demand-supply paradox in AI and ML jobs is the lack of knowledge of where to begin, what to study, and how to master the concepts of AI and ML.
Worry not, for that’s precisely what we’ll help you decode today!
How to break the ice with AI and ML?
- Understand what AI and ML mean.
Before you break bread with the complex concepts of AI and ML, first you must understand what they mean – you need to get familiar with their essence.
Artificial Intelligence is a broader umbrella that encompasses Machine Learning. AI refers to “the science and engineering of making intelligent machines that have the ability to achieve goals like humans do.” On the contrary, Machine Learning is a subset of AI “that gives computers the ability to learn without being explicitly programmed.” In other words, ML refers to the technique of parsing data and analyzing it to learn from its hidden patterns and apply those learning to make informed and smart decisions.”
Thus, AI is more of an ‘idea’ – to create machines that utilize intelligence and cognition to accomplish a host of human-specific tasks – whereas ML is the technology that gives meaning to this idea.
- Master coding with Python.
AI and ML are primarily about applying the various principles of Mathematics, Statistics, and Computer Science to data. Coding is an integral part of ML algorithms and AI applications. So, you must master coding in at least one programming language, most preferably Python.
The reason we recommend Python over other programming languages is that, when it comes to ML, Python is the golden choice. The language is naturally disposed towards ML and is favoured by many tech companies requiring end-to-end integration and development of analytics-based applications. Plus, Python has a host of libraries suited for every ML need. For instance, you have Pandas for data extraction and data munging; Matplotlib and Seaborn for data visualization, and Theano and Tensorflow for Deep Learning.
Natural Language Processing
- Brush up on your Mathematical and Statistical knowledge.
Maths and Statistics make up the foundation for numerous ML algorithms. You must be well-versed with a few select areas in Mathematics and Statistics including Linear Algebra, Multivariate Calculus, Conditional Probability, Bayesian Probability, Descriptive Statistics, and Inferential Statistics, Hypothesis Testing, Algorithms and Complex Optimizations, to name a few.
- Enroll in an online course.
After you have successfully completed the first three steps, it’s time to choose a well-designed course that juxtaposes both AI and ML into one. While there is no dearth of online courses on ML and AI, the trick is to choose the right one.
Since you wish to learn both AI and ML simultaneously, you must choose such a program that will cover both the concepts equally. Learning through a well-designed and structured curriculum of an online program will help you adopt a systematic approach to learning. Also, you will get the opportunity to connect and interact with a vast network of people spread across the country/globe, all hailing from different backgrounds. Networking is always great.
After the completion of the course, you’ll receive an online certification which will look good on your Resume!
5. Get handsy.
All this while, we have been focusing on acquiring all the theoretical knowledge of AI and ML. Now, it’s time to implement and execute that knowledge. The best way to start practising and build data models is with datasets. Two of the great sources to gain hands-on experience with datasets are:
a) UCI Machine Learning Repository – This repository comprises around 430 diverse datasets, exclusively curated for Machine Learning and intelligent systems. You can search datasets by industry, tasks, dataset size, and so on.
b) Kaggle – The name alone is enough. For aspirants who wish to hone their AI and ML skills, Kaggle is the perfect platform. It is a competitive forum that hosts coding and ML hackathons and competitions, which are excellent to gain practical coding experience, engage in teamwork, and get insider knowledge about your competitors. Participating in platforms like Kaggle has another bonus – since potential recruiting companies regularly monitor such sites, you also get the much-needed exposure!
Granted that AI and ML are not the easiest of things, but mastering them is not impossible either. All you need is the right guide and approach. And now that we’ve given you a detailed guide on how to get started with AI and ML, it’s time to get cracking on it!