Machine Learning

The role of analytics, statistics, and machine learning in data science life cycle

The role of analytics, statistics, and machine learning in data science life cycle

As we all know the field of data science has emerged as a transformative force in the recent years. But what exactly falls under the data science umbrella? This blogs uncovers the core disciplines - analytics, statistics, and machine learning - exploring their roles throughout the data science life cycle (CRISP-DM) . You'll learn the distinctions as well as their interconnected roles in driving a structured data science approach. 

Humans in Machine Learning - you think you know it?

Humans in Machine Learning - you think you know it?

The general perception around Artificial Intelligence and Human involvement in it is very limited, to say the least. When we talk about general-purpose AI, we view it as a system that can sort of solving any problem that it sees. Thus, there is a tendency to believe that AI and Machine Learning will fully replace human work. But we are quite some way away from that - most AI systems we see today are narrow in their remit but very good in that narrow space. One might argue that as AI systems start to take over multiple narrow functions within an organisation, the human workload will eventually reduce. But we have to understand that humans do not possess a singular type of intelligence but exhibit multifarious types of intelligence.