Loading

Machine Learning

Demystifying Machine Learning. The Complete Machine Learning Developer Course 2022 [Videos].

Machine Learning”. Now thats a word that packs a punch! Machine learning is hot stuff these days! And why wont it be? Almost every “enticing” new development in the field of Computer Science and Software Development in general has something related to machine learning behind the veils. Microsofts Cortana – Machine Learning. Object and Face Recognition – Machine Learning and Computer Vision. Advanced UX improvement programs – Machine Learning (yes!. The Amazon product recommendation you just got was the number crunching effort of some Machine Learning Algorithm).

And not even just that. Machine Learning and Data Science in general is EVERYWHERE. It is as omnipotent as God himself, had he been into Computers! Why? Because Data is everywhere!

So it is natural, that anyone who has above average brains and can differentiate between Programming Paradigms by taking a sneak-peek at Code, is intrigued by Machine Learning.

But what is Machine Learning? And how big is Machine Learning? Lets demystify Machine Learning, once and for all. And to do that, rather than presenting technical specifications, well follow a “Understand by Example” approach.

Machine Learning : What is it really?

Well, Machine Learning is a subfield of Artificial Intelligence which evolved from Pattern Recognition and Computational Learning theory. Arthur Lee Samuel defines Machine Learning as: Field of study that gives computers the ability to learn without being explicitly programmed.

So, basically, the field of Computer Science and Artificial intelligence that “learns” from data without human intervention.

But this view has a flaw. As a result of this perception, whenever the word Machine Learning is thrown around, people usually think of “A.I.” and “Neural Networks that can mimic Human brains ( as of now, that is not possible)”, Self Driving Cars and what not. But Machine Learning is far beyond that. Below we uncover some expected and some generally not expected facets of Modern Computing where Machine Learning is in action.

Machine Learning: The Expected

Well start with some places where you might expect Machine Learning to play a part.

  1. Speech Recognition (Natural Language Processing in more technical terms) : You talk to Cortana on Windows Devices. But how does it understand what you say? Along comes the field of Natural Language Processing, or N.L.P. It deals with the study of interactions between Machines and Humans, via Linguistics. Guess what is at the heart of NLP: Machine Learning Algorithms and Systems ( Hidden Markov Models being one).
  1. Computer Vision : Computer Vision is a subfield of AI which deals with a Machines (probable) interpretation of the Real World. In other words, all Facial Recognition, Pattern Recognition, Character Recognition Techniques belong to Computer Vision. And Machine Learning once again, with it wide range of Algorithms, is at the heart of Computer Vision.
  1. Googles Self Driving Car : Well. You can imagine what drives it actually. More Machine Learning goodness.

But these were expected applications. Even a naysayer would have a good insight about these feats of technology being brought to life by some “mystical (and extremely hard) mind crunching Computer wizardry”.

Machine Learning : The Unexpected

Lets visit some places normal folks would not really associate easily with Machine Learning:

  1. Amazons Product Recommendations: Ever wondered how Amazon always has a recommendation that just tempts you to lighten your wallet. Well, thats a Machine Learning Algorithm(s) called “Recommender Systems” working in the backdrop. It learns every users personal preferences and makes recommendations according to that.
  1. Youtube/Netflix : They work just as above!
  1. Data Mining / Big Data : This might not be so much of a shock to many. But Data Mining and Big Data are just manifestations of studying and learning from data at a larger scale. And wherever theres the objective of extracting information from data, youll find Machine Learning lurking nearby.
  1. Stock Market/Housing Finance/Real Estate : All of these fields, incorporate a lot of Machine Learning systems in order to better assess the market, namely “Regression Techniques”, for things as mediocre as predicting the price of a House, to predicting and analyzing stock market trends.

So as you might have seen now. Machine Learning actually is everywhere. From Research and Development to improving business of Small Companies. It is everywhere. And hence it makes up for quite a career option, as the industry is on the rise and is the boon is not stopping any time soon.

So, this is it for now. This wraps up our Machine Learning 101. Well hopefully meet again, and when we do, well dive into some technical details of Machine Learning, what tools are used in the industry, and how to start your journey to Machine Learning prowess. Till then, Code Away!

See All

Comments (154 Comments)

Submit Your Comment

See All Posts

Related Posts

Machine Learning / Youtube

What is machine learning in simple words?

Learning means the acquisition of knowledge or skills through study or experience. Based on this, we can define machine learning (ML) as follows: It may be defined as the field of computer science, more specifically an application of artificial intelligence, which provides computer systems the ability to learn with data and improve from experience without being explicitly programmed. Basically, the main focus of machine learning is to allow the computers learn automatically without human intervention. Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems.
27-jan-2021 /10 /154

Machine Learning / Youtube

What is sequence data in machine learning?

Sequence Modeling is the task of predicting what word/letter comes next. Unlike the FNN and CNN, in sequence modeling, the current output is dependent on the previous input and the length of the input is not fixed. In this section, we will discuss some of the practical applications of sequence modeling.
3-jan-2022 /10 /154

Machine Learning / Youtube

What is descriptive statistics in machine learning?

DESCRIPTIVE STATISTICS : Descriptive Statistics is a statistics or a measure that describes the data. INFERENTIAL STATISTICS : Using a random sample of data taken from a population to describe and make inferences about the population is called Inferential Statistics.
3-jan-2022 /10 /154