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Machine Learning Basics | What Is Machine Learning? | Introduction To Machine Learning | Simplilearn

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This Machine Learning basics video will help you understand what is Machine Learning, what are the types of Machine Learning – supervised, unsupervised & reinforcement learning, how Machine Learning works with simple examples, and will also explain how Machine Learning is being used in various industries. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. This is possible as programs learn from previous computations and use “pattern recognition” to produce reliable results. Machine learning is starting to reshape how we live, and it’s time we understood what it is and why it matters. Now, let us deep dive into this short video and understand the basics of Machine Learning.

Below topics are explained in this Machine Learning basics video:
1. What is Machine Learning? ( 00:21 )
2. Types of Machine Learning ( 02:43 )
2. What is Supervised Learning? ( 02:53 )
3. What is Unsupervised Learning? ( 03:46 )
4. What is Reinforcement Learning? ( 04:37 )
5. Machine Learning applications ( 06:25 )

Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1

Download the Machine Learning Career Guide to explore and step into the exciting world of Machine Learning, and follow the path towards your dream career- https://www.simplilearn.com/machine-learning-career-guide-pdf?utm_campaign=Machine-Learning-Basics-ukzFI9rgwfU&utm_medium=Tutorials&utm_source=youtube

Watch more videos on Machine Learning: https://www.youtube.com/watch?v=7JhjINPwfYQ&list=PLEiEAq2VkUULYYgj13YHUWmRePqiu8Ddy

#MachineLearning #MachineLearningAlgorithms #DataScience #SimplilearnMachineLearning #MachineLearningCourse

About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with the knowledge and hands-on skills required for certification and job competency in Machine Learning.

Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.

What skills will you learn from this Machine Learning course?

By the end of this Machine Learning course, you will be able to:

1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire a thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems

We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning

Learn more at: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Machine-Learning-Basics-ukzFI9rgwfU&utm_medium=Tutorials&utm_source=youtube

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48 thoughts on “Machine Learning Basics | What Is Machine Learning? | Introduction To Machine Learning | Simplilearn”

  7. I am starting a research into Machine Learning in Animation, but first I need to grasp the basics of Machine Learning! This video already helped me a lot, does anyone else have good videos or articles that can explain the basics…"for dummies"?

  10. Hey,in the coin example who's the one giving input about the weights of the coins,if we are the one providing the inputs then we can bifurcate the coins easily with normal algorithm,then how ML comes into picture in this scenario?

  13. 1. Supervised
    2. Supervised
    3. Unsupervised
    Example:
    Spam mails, Google search page, Google search options, Advertisements on social media, word suggestion in android key board etc

  14. The problem begins when the human has no clue how he decides if he likes the music or not and teaches the wrong algo to the machine. That's called a "step-back".

  19. In Facebook it frequently gives some images of some product with it's prize, whatever the product we frequently searched on online shopping apps.

  21. In Scenario 3: I hope it should be supervised learning, how will a computer know whether a transaction is fraudulent or not, without anyone flagging them. But u said it is unsupervised learning.

  22. very nice vid! but some of my classmates watching this tho, they were so annoying asking so much questions about wickets, bowlers, crickets and batsmen. so i though maybe you should've used a more popular sport (or just about anything more popular than cricket) besides cricket as an example for the Unsupervised Learning part. anyways, thank you very much!

  23. This video is quiet frankly down to point. I was even excited when I begun this field and the different things you could indulge in and improve for a business. It really is helping me and my career. I am even starting my own channel to breakdown some of the concepts that I found hard to understand about different algorithms and how they work. Check it out and for any starters, do tell me what you find hard at first to grasp when begging into the field ☺️

  29. I wonder for how long will humans be still needed in this loop..those jobs are high pay and there is a constantly growing shortage of employees in the field of ML and AI … but for how long will a human be needed to put in the tasks for the ML to work with…same goes for programmers and coders…to me it seems as if those jobs will be completely wiped out in a decade or so

  37. well the vid would be more intelligible if the speaker fluent in ENGLISH, not to bash but your pronunciatuon was kida hard for a forginer like me to understand

  38. Hai….after completing this course can i participate in kaggle competitions….
    I used supervised learning to decide:
    1. Supervised.
    2. Supervised.
    3. Unsupervised.

  41. Around me up to now I saw one thing it's robotic house when we enter the room automatically fans will switch on and etc from this we can save a lot of power we are absent in home

  43. ..humans do learn from past experiences but that alone stifles innovation and problem solving..we are good at learning about the right now too..

  44. 1) supervised machine learning
    2) supervised machine learning
    3) unsupervised machine learning

    Examples of machine learning in my day to day life
    1) Google asking for review of all the places I visited like temple, restaurant, tourist places
    2) android calendar reminding me credit card bill by checking my sms
    3) weather forecast

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