The words AI, Machine Learning, Neural Networks have grabbed the attention of the whole world. They say, "AI is the new Electricity". Years ago, electricity had a dramatic impact on every aspect of our life. AI too is expected to change every aspect of our life in the years to come.
In this series of posts on Machine Learning, I will touch the subject at various levels, starting with the most basic non technical introduction to the subject. Then going deeper in to the subject, to include various aspects of implementation, algorithms, and recent research. This series will continue to grow as I continue to venture into the domain.The Basics
These posts give you a very basic idea about what these terms mean. There is a very little technical content out here. More of basic concepts for someone who has no idea about the subject.
It is important to understand some core mathematical concepts before we go understand Machine Learning. Most of us would have studied these in school, but forgotten over time. Here is a refresher of the core concepts.
Concepts in mind have little value until they are translated into code. One has to be an expert coder in order to proceed beyond the concepts. Python is the most popular language for Machine Learning. Python has a wide range of libraries and frameworks for implementing AI algorithms.
Python is the single most popular language for AI. But several other languages have provided their share of libraries that can be used for AI.
Neural Networks are great, but often an overkill for a simple problem. Not just for the AI model developed, it is important that we truly understand the concepts of Statistical Machine Learning before we try to mess around with Neural Networks. With that in mind, these posts survey the core concepts of statistical machine learning
This is perhaps the hottest technology of the decade - that has opened the doors to an infinite range of opportunities for us. These posts look into different aspects of neural networks and their development.
Training a Neural Network is never as straight forward as it may sound. Data is never what we want it to be. And just like kids, the networks have a strong tendency to learning what we don't want them to learn. It is necessary to guide them to the appropriate learning.
AI is a very old concept. But it was restricted to academic research. The recent explosion in data and computational capacity has brought forward a lot that was restricted to research papers.