Machine Learning

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Big Data, Machine Learning, Artificial Intelligence, etc. 
If you are regular with news, then you must have heard a lot about these words. Let’s try to understand things with examples

What is Big Data?
In 1990s the size of data used to be small and people used to store only relevant data points. With the World Wide Web(WWW) boom, data became omnipresent. There was a way to store a good amount of data in Excel files and other applications. But the major change happened with the advancement in mobile technologies.
Smartphones came up with a lot of data. Every application and website is storing a plethora of data ranging from your personal to professional information. Almost all the clicks you make on the internet are being stored somewhere in the word. 
When you are working with a lot of data, then that data is termed as Big Data.

So, it’s not like Big Data is a new concept. It’s just that the size of data increased multiple times and in order to store these data, we needed new tools and technologies. All this complete eco-system is called Big Data.

Now, what is Machine Learning?
Machine Learning is a way to train a machine to start learning from the user’s behavior and then provide useful information or take actions accordingly. You can see Machine Learning examples around you.

1. You click an advertisement on Google and the next day you get similar ads. This is because your interest was tagged in this brief span of time and now you are bombarded with the advertisements.

2. Ever heard of Driverless cars? Can you even imagine the rate at which the back-end algorithms need to work in order to identify an object and taking actions accordingly? The margin of error is almost zero because we are talking about real life. This is where image recognition and several different algorithms come into the picture.

3. Machine Learning is learning from data, on the other hand, Artificial Intelligence is a buzz word. There are so many problems which you can solve using machine learning. You will understand the capabilities of this domain in the coming Days

4. 10 Years back Software Engineers used to work on these predictive models, clustering and classifying data, etc. But as the amount of data started increasing, handling data and getting insights from these data because difficult. This gave rise to new job opportunities which go by the name of Data Scientist, Data Analyst, Decision Scientist, Big Data Analyst, etc.
So, this thing is not new, it just got scaled up

5. Most of the hard work for machine learning is data transformation. From reading the hype about new machine learning techniques, you might think that machine learning is mostly about selecting and tuning algorithms. The reality is more prosaic: most of your time and effort goes into data cleansing and feature engineering — that is, transforming raw features into features that better represent the signal in your data. 

6. AI is not going to become self-aware, rise up, and destroy humanity. A surprising number of people seem to be getting their ideas about artificial intelligence from science fiction movies. We should be inspired by science fiction, but not so credulous that we mistake it for reality. There are enough real and present dangers to worry about, from consciously evil human beings to unconsciously biased machine learning models. So you can stop worrying about skynet and superintelligence .

7. ML is a computer science discipline that consists in making computers “learn” from data rather than programming instructions. For example, imagine you had to implement a gender (male vs female) recognition software. If you had to implement this in the traditional way, you would need to extract features that would help you decide. Then, you would write a lot of code to instruct the computer how to use these features. Unfortunately, this approach is tedious and not robust enough. On the other hand, the ML approach consists in collecting lots of images and labeling them. Then, running an ML algorithm that will learn the task by observing the data. By the way, this approach is called supervised learning.

8. ML is an evolving and exciting field. Many jobs exist and many more will. It is the modern form of literacy in our technological and data-driven society. Learn about it as much as you can.

9. You can very well make a career in Machine Learning and Data Science. You just have to practice playing with data and understanding the data. In my personal opinion, Machine Learning is here to stay, so it’s better if you take some time to understand it

It’s neither possible nor effective to put every topic under the same belt. Below are the links to our Machine Learning repository

NLP Interview Questions
Feature Engineering in Python
Supervised Learning overview
Exponential Smoothing(ETS) Forecasting in layman’s terms
Supervised Learning Questions

Keep Learning 🙂

The Data Monk