Beginning a Career in Data Science?
Are you fascinated by the capabilities of Artificial Intelligence? or do you belong to the category of people who believe that Artificial intelligence could be humanity’s downfall.
I came to Medium to build and transfer my knowledge on Data Science . The countless use cases of AI by Tony Stark in Marvel Comics has got me drooling over this technology. The dream of most of the fans was to own the suit or J.A.R.V.I.S. I crave for the latter.
Introduction to the field of Data Science got me closer to achieving my dream of Building a Smart Bot/Assistant. I explored the subject through online certification programs conducted by Udacity, Udemy and Nptel, each well crafted in an unique way to begin my career.
Buckle your belts, there’s a roller coaster of topics that shall be brought to discussion. Starting from the simplest Machine Learning Concepts to building your very own Deep Learning Neural Networks. I am fortunate enough to be travelling with you throughout this journey.
This shall be dealt in an organised manner by splitting into two series :
Machine Learning
In this series of posts you will get a high level look at the field of machine learning. We shall also discuss some emerging technologies and the machine learning applications that will change much of how the world works today from health, to transportation, to communication.
Deep Learning
Deep learning has beaten essentially all other machine learning algorithms in its ability to predict. It can be used for Supervised, Unsupervised as well as Reinforcement Learning. The growth of this technique makes alot of sense. Overtime we cared less and less about how we make predictions, and now we care about the accuracy of our predictions. This connects well to the advancement of deep learning where we rarely understand all of how and why our model make certain predictions.
Unfortunately, Deep Learning is not the ideal solution to every given problem. It has 3 major barrier.
- They require a lot of data.
- You must have the computing power. In order to reach superhuman performance in the game of Go. Google’s DeepMind AlphaGo required 1202 Cpu’s and 176 Gpu’s
- You probably won’t understand why certain decisions were being made, given the complexity and flexibility of these problems.
I shall also discuss several other topics closely related to the field of Artificial Intelligence. Hoping topics that confuse me, confuses other too.
Here is the link to the Machine Learning topic ( Linear Regression ) :
You can also, choose to read through some really interesting Case Studies.
