Source – https://analyticsindiamag.com/
Both AI and data science make use of machine learning techniques. Also, both fields use the same data science tools to clean, process and analyse data.
Data Science, Machine Learning, and Artificial Intelligence are the significant drivers of the fourth industrial revolution. Since data powers all these fields, they are often used interchangeably. However, despite the similarities, Data Science, ML and AI are different from each other.
Data Science is a multidisciplinary field with a focus on the use of data to derive insights. A good data scientist must possess a wide range of skills, including programming, mathematics, and domain knowledge of the desired field of application. To analyse data, make inferences or forecast predictions, data scientists use techniques like statistical modelling, hypothesis testing, data visualisations, and machine learning.
Artificial Intelligence is a broader concept and is often used to describe machines capable of replicating the human mind’s cognitive capabilities. Broadly, AI is divided into two parts: general AI and narrow AI. General AI refers to a machine’s ability to think and function as a human. It is still a very fictional concept, and experts believe it would be some time before it is ultimately realised. On the other hand, narrow AI systems accomplish singular or limited tasks using machine learning.
End of the day, both AI and data science make use of machine learning techniques. Also, both fields use the same data science tools to clean, process and analyse data.
Application Oriented AI/ML Specialist
If you want to become an AI/ML specialist and work on real-world applications, you need to learn how to implement machine learning or neural networks. Practical and effective applications begin with data, and knowing just the theoretical concepts won’t cut it. An AI/ML practitioner should learn various aspects of data science and machine learning to apply in real-world settings to meet tangible business objectives.
The best place to start is to learn tools like Python, related data science libraries and techniques like data extraction and data wrangling. A deeper understanding of essential statistical concepts, supervised and unsupervised machine learning and deep learning techniques is also crucial.
To get you started for a career in this direction, AnalytixLabs offers one of the top courses – ‘Applied AI’, which is also suitable for beginners.
The course will help you break into the AI/ML domain. The program will teach some of the most sought-after tools and libraries, including Python, Numpy, Pandas and Scikit-Learn.
The curriculum covers data handling, visualisation, statistical modelling, machine learning and advanced use of deep learning for AI-based applications like image processing, text data processing, chat-bots, time series, recommendation systems, machine translation, IoT, etc.
Individuals who want to work on AI and ML deployment should focus more on courses in AI Engineering. For this, you need to learn everything from the building blocks of AI Engineering to AI Productisation and Solutions, along with Cloud Computing and Machine/Deep Learning concepts.
AI is a set of mathematical algorithms that enable machines to understand and analyse the correlations between various data elements. Hence, it is also essential for AI Engineers to understand data science fundamentals and concepts in programming and mathematics.
AI engineers should continuously learn and adapt to the rapidly evolving world of AI and learn Data Science tools with a hands-on approach.
AnalytixLabs offers an Artificial Intelligence Engineering course best suited for IT professionals who want to focus on Artificial Intelligence and ML deployment.
The course will introduce you to the foundations of AI Engineering with modules in Data Science, Python, Cloud Computing, etc. The syllabus includes data pipeline creation and implementation of machine/deep learning models and an overview of the deployment methodologies like Flask framework, Web app creation, and Docker/Kubernetes implementation.
A data scientist, AI/ML specialist, statistical analyst, or analytics consultant requires more application-oriented skills in AI. They must know tools and libraries in Data Science to build models in AI/ML for building advanced applications.
To work as an AI/ML engineer, one needs to learn how to build and deploy intelligence in machines. For this, they need to have a thorough understanding of the building blocks of AI Engineering and the fundamentals of Data Science.