There are a plethora of job roles that fall under data science but the most common field that we often hear about is — Artificial Intelligence and Machine Learning. As the competition strengthens the job market, employers are still struggling to find the right talent. Every company today, be it financial services or e-commerce companies they all are looking to hire the best talent skilled in artificial intelligence and machine learning.
But it is very important for one to first understand the difference between these two job roles. Artificial Intelligence and machine learning have become an integral part of many businesses. However, the terms are used interchangeably, let us look at the differences.
The differences between AI and Machine Learning
Artificial intelligence is all about computer intelligence. In simple terms, it is a broader concept of machines that carry out tasks in a way that is considered – smart. As technology advances, computers are trained in such a manner that they understand how our minds work. You probably have seen it unfold right in front of your eyes, from self-driving cars to Google brain it is because of the impact of artificial intelligence.
In short, artificial intelligence is a study that develops machines and software exhibiting human intelligence.
The role of an artificial intelligence engineer, specialist etc. is to program computers to be able to test the hypothesis in relation to how the human brain works, through cognitive simulation. They cover various aspects that range from facial recognition, recognising the voice that helps solve complicated problems.
To start a career in AI, this is how you should go about it:-
- Learn a programming language – Python is the most favorable language in AI, you can also code AI applications in Java or C++ etc. Linear algebra and calculus
- Build your first AI bots
- AI is a huge field, you need to first learn the subjects that fall under the sub-field like –
- Neural networks
- Evolutionary computation
- Speech processing
- Expert systems
- Machine learning
- Natural language processing etc.
Machine learning is a sub-field of AI, machines take the data that is retrieved and learn it for themselves. It is one of the promising tools in AI today. Machine learning helps the system learn and recognise the pattern on its own to further make predictions.
A Machine learning engineer is neither a data scientists nor a data engineer, he is one who sits in the crossroads of both these job roles. They are engineers who are cross-trained to become proficient both with data engineering and data science. A machine learning engineer takes what a data scientist finds and make it production worthy by using algorithms which can be either an ML or an AI code to instill results. If the results go haywire, incorrect or distorted either way a machine learning engineer will the one who will be on the lookout to make changes in their model that would require tweaking or retraining.
To start a career in machine learning, this is how you should go about it:-
- Learn programming languages like C++, this can help speed your coding skills. R is used for statistical problems.
- Learn Statistics
- Machine learning algorithms
- Data modeling
- Data evaluation
- Distributed computing
How can you start your career in the latest trending technologies?
Update yourself by taking up certification courses and credentials in technologies such as artificial intelligence and machine learning. But unless you already have a strong quantitative background the pathway to these careers can be challenging but not impossible.
Here are some great certifications credentials you can consider learning from:
- Cloudera – Cloudera covers these topics under the data science program. Participants of the program or workshop must have a basic understanding of concepts such as machine learning algorithms, Python, R, and statistical modeling etc.
- Coursera – The Coursera course covers topics such as data mining, statistical pattern recognition, supervised and unsupervised learning, machine learning etc. The course also discusses topics such as how to apply algorithms in building robots, text understanding, database understanding etc.
- Artificial Intelligence Board of America – The Artificial Intelligence Board of America (ARTIBA) provides the credentialing framework that most organisations look for when hiring. Since it is quite difficult for an employer to believe the skills the candidate possess it is important that one needs to showcase their skills through the credentials that one acquires. The skills you learn covers Machine learning, Neural networks, Regression methods, NLP, Cognitive computing and Deep learning etc.