Skip to main content

Introduction to ML

By

Greetings buddies

Today we are going to see what is machine learning, why it is trending these days, why it is used and its applications. As we all know that the word machine learning is hyped and often used in technical conversations or when someone is boasting about their skills.

But do we actually know it?? Maybe someone reading this blog does know it yet half of the students here may not know.

So what is machine learning then? Is it some thing where people sit and let machine do task on it's own?? Or is it something where machine gets it's own brain?? Is it so??

The answer for this question is NO. Machine learning is task where computer/machine focuses on data to use them, find patterns, build logic, use math functions and give some output based on those data & math functions. It is true that math is one of the base pillar on which ML is developed. Our system dose not understand anything except math. So to make some sense from data, scientists have developed functions which when given to machine with data results into some patterns. This patterns then are again analyzed and used to predict values.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

Machine learning enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information.

Here our 1st question is answered. Now the question is why it is trending. As we all know that these days there is a huge amount of data generated every second. We have servers to handle them but why do we store it ? Any ideas ? Here is one small idea answering that why we need big data here.

Okay so the above answer cleared few confusions. But created another one that where is this linked with ML yes? Well human have mind(many don't use it tho) but processing this much data i mean petabytes of data and finding patterns and predicting trends is almost impossible for us. So the answer for why ML is simple, to help human in processing data which he can't process normally.

As it is our mentality, we kept upgrading from just finding patterns to image processing, text extraction and finally to automation. This answered our 2nd question.

Now the applications of ML, where on the earth it is been used. I am sure you will get many answers on Quora, i will try my best to cover them all here. Hey, have you every seen chat-bots?? They are real time application of ML. Ever thought why amazon is able to give you products based on your recent purchase. That's because they use patterns and analyze the data based on each user and try to lure them into purchasing product related to your last one. Netflix does the same like amazon but with movies and series

Businesses decide based on their recent sale that what kind of product mixture they have to put in market so they get best out of their production and customers totally based on ML. Financial advisor are also trying to use ML these days to see what kind of investment will yield them better results but they can't rely on it because investment has many risks and machine cannot study them all at once so in this sector it is very much limited. Yet it is trying to clutch a tight grip around many domains like Health, Securities, Research, Space Tech & many more.

I know it's too much reading now so we will conclude here, hope you get what is ML & why ML. In next post we will start learning basics of ML taking python lessons too with us so there is no need to go elsewhere for resource. Please leave comments and views below. Thank you & have a nice day.

Comments

  1. shiwangi mishraNovember 19, 2018 at 9:57 AM

    How do you know which Machine Learning model you should use?

    ReplyDelete
    Replies
    1. AkshayNovember 19, 2018 at 11:47 PM

      I have written a post regarding your answers today. Link to it is mentioned here. Read it and if still have doubt reach me out.
      https://mymlleactures.blogspot.com/2018/11/how-to-choose-machine-learning-model.html

      Delete

Post a Comment

Popular posts from this blog

How to choose Machine Learning model for your problem ??

By
Greetings fellas, Here we will discuss the above mentioned question.   Machine learning is part art and part science. When you look at machine learning algorithms, there is no one solution or one approach that fits all. There are several factors that can affect your decision to choose a machine learning algorithm. Some problems are very specific and require a unique approach. E.g. if you look at a recommender system, it’s a very common type of machine learning algorithm and it solves a very specific kind of problem. While some other problems are very open and need a trial & error approach. Supervised learning, classification and regression etc. are very open. They could be used in anomaly detection, or they could be used to build more general sorts of predictive models. Understand Your Data The type and kind of data we have plays a key role in deciding which algorithm to use. Some algorithms can work with smaller sample sets while others re...
1 comment
Read more

Kaggle's informative Tutorial

By
Greetings, I would like to declare that this is just an Informative post. No promotion of site or any of its partner is done here. Would share this kind of platforms regularly with everyone so one can code online & learn.  Today we will see how to use kaggle . What is kaggle? Well it is an site where MNCs host competitions to find solutions of their problems based on ML & AI. Also it is one of the best platforms to learn Python, R, Machine Learning & many more stuffs. It has its own coding environment like Jupyter Notebooks, Which is known as Kernel. This kernel supports both R & Python. In this kernels you can code freely and develop your own code & run it there itself as it gives 14gb RAM & 5.2gb GPU for each session. Let's see how we can leverage power of kaggle for learning. You need to make account first which only require an active E-Mail id. Then after signing in. You will see dashboard like this. My Kaggle Newsfeed Competitions Ma...
Post a Comment
Read more