First step Towards Machine Learning

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The human pace is getting lazy day by day and wants an ease in their life. For that, we created machines, machines that are capable enough to take commands and perform tasks for us.
But what if machines can think and take decisions on their own……Sounds rubbish!!!
In this modern era of machines and technology, this thing is actually taking place. Big companies like Google and Facebook are actually doing these miracles…
Means, have you ever thought that while uploading pics on Facebook, the computer automatically detects faces and stats suggesting your friend’s name for tagging or have you ever thought while checking flights on google for a particular destination, how you start getting emails for flight related offers.

At least once in your lifetime, you must have seen at least one Marvel’s movie and you must also know that why these movies are so famous across the globe. I will talk not much about all movies but there is particularly one movie named as “Iron man”, which is so popular because it takes you to a virtual world where a machine can do such cool stuff, but when you note some points and ask “how?” then you will come to know that behind that amazing suit there is a super computer that is actually doing all the things.
So we can say if a computer can make decisions by analyzing the facts then we may be able to do miracles with it.


All these facts amazed me and I also decided to start learning “The Machine Learning”, but it was not that easy, because in machine learning if you are not doing some expensive course for it, you will not find any starting point.
When you will start searching for some material you will find many algorithms, concepts, books and many things, and you will find these things very complex as you will get to know that before starting these concepts you will need some maths stuff also.

So I decided to write this blog, this blog will not give you names of heavy books or some big names of algorithms, but this blog will give you a gist of all things that are needed before you dive into machine learning world.

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In calculus, you need some concept of differentiation especially chain rule and you also need integration. All these concepts will help you to understand various algorithms that are using these concepts.


Probability theory is a mathematical framework for representing uncertain statements. It provides a means of quantifying uncertainty and axioms for deriving new uncertain statements. In artificial intelligence applications, we use probability theory in two major ways.

  • First, the laws of probability tell us how AI systems should reason, so we design our algorithms to compute or approximate various expressions derived using probability theory.
  • Second, we can use probability and statistics to theoretically analyze the behavior of proposed AI systems.

In probability starting from basics you have to cover some distributions like Bernoulli Distribution, Multinoulli Distribution etc.

You also need some basics of  Permutations & combinations so that you can apply these concepts in probability and some algorithms as well.

After basic of these things you may be able to jump into learning algorithms and these concepts will also help you to understand the complexity and build your own algorithms.


This blog is to list out a small thing that is required before start learning  “The Machine Learning”. If you just don’t want to use already developed machine learning algorithms and want to go into depth of concepts so that you can learn the actual working of those algorithms, then this blog is for you, In this blog I summarised the overall idea behind the learning and it’s motivation towards ML, in our next blogs we will be going into the details of ML with their implementations from basics to advance.


Written by 

Nitin Aggarwal is a software consultant at Knoldus Software INC having more than 1.5 years of experience. Nitin likes to explore new technologies and learn new things every day. He loves watching cricket, marvels movies, playing guitar and exploring new places. Nitin is familiar with programming languages such as Java, Scala, C, C++, Html, CSS, technologies like lagom, Akka, Kafka, spark, and databases like Cassandra, MySql, PostgreSQL, graph DB like Titan DB.

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