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.

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 )

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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.

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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

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39 Comments

Olayemi Ige

i understood the concept of machine learning in less than 10 mins. thank you.

Evgeniy Mamchenko

You can train your machine learning model for image classification even without writing any code in an Android app called Pocket AutoML. It trains a model right on your phone without sending your photos to some "cloud" so it can even work offline.

Gopala Krishnan S / 6302

Scenario 1 and 2 is supervised
Scenario 3 is unsupervised

Sachit Jhurani

an everyday example of machine learning:- Alexa just this song & play the previous one because I don't like this song. Then Alexa removes the song from his recommendation queue & play the previous one.

Great teacher. Great teaching skills. Try to add quiz question after explaining a concept on your upcoming videos, it really helps us to test our understanding on that topic. By d way great explanation =.

Adoan Mian

Senario one= reinforcement learning, Senario 2= unsupervised learning and scenario 3 = supervised learning.

In scenario 1 you try to find the name of your friend for example, but after you searched it on Facebook, you'll see that many members has exact the same name. Thus, you give feedback to use the filter-model of it in order to find the correct person with the correct characteristics. That's why the first one is Reinforcement Learning.

2nd scenario, when you want to find the movie on Netflix which you like, you'll see that there so many movies you like to watch. However, you just want to focus on the quality of the movie ( like the tempo, the resolution, audio accuracy) although you do not know the label. That's why it is unsupervised.

Finally, the last one is supervised because you know that in your computer there are intruders, like viruses or hackers, which can be detected based on the model it's designed to find the feature, which the degree of danger, and the label, which is a virus, hackers, spyware, and many more.

I have so much contentment about that topic which is briefly and clearly explained for everyone who wouldn't even know that topic. The examples also improved my understanding of the meaning of Machine Learning which I appreciate it the most. Hopefully, many of us will learn from this enjoyable and fascinating lesson. Keep it up! 🙂

Rajarshi Das

Everyone who is interested in machine learning, you must understand
1) Machine learning is an algorithm that depends on AI to take decisions.
2) Data Science works on collecting, storing and analyzing data for information. You can later use machine learning to classify the data into categories.
3) You can start programming by downloading the Anaconda package.
Happy Coding

DIVYANSH TRIPATHI

Thanks for the video , i understood the crux of ML with such ease!

MANU ABARAHAM

I am reading 21 lessons for 21st century ..these words are often coming …it really helpful

Ph.D View360

I admire your teaching skill. The reason why simplilear is the first choice of the learner.

Sreeya Aladanda

youtube itself is the best example of machine learning ..because it automatically recommends the videos based on our past history!!!

Yasasvi Upadrasta qBJHdjhKMy

1,2 are supervised learning. 3 is reinforcement learning in Quiz.. Video was good, understanding the concepts.. Thank you..

Priyanshi Bhattacharjee

This is the first video of yours I'm watching, and it's so good that I subscribed right away 💯

Tamal Majumder

god bless , such an wonderful explanation 🙂 HATS OFF

Billy Clinton Muguai

I understood it so well. Thanks.❤️❤️❤️

Jatin Negi

scenario 1 is supervised learning
scenario 2 is supervised learning
scenario 3 is unsupervised learning

Atchuth Paraselli

Loved the way of explaining with good examples
Hope you would be my teacher lol😂

Vaishnavi Nigade

Do Phone Pay and Google Pay use machine learning? And if so, how?

Lijie Di

What the difference between traditional statistical model with machine learning

After watching ur video I got interest in learning machine learning
Such an crystal clear explanation 🙂

hotdogvlogs

wow! this is my first time actually researching this topic being a computer science student. i have got to say, this really brightened my mood and brought some light to my day/mind regarding my major! 🙂 awesome stuff!

Chris Evans

Love hearing you speak. You could read a dinner menu and I'd still get chills.

vit bpl vimal

Ur video is informative & superb explaintation …. thank a lot

Jacob Southall

scenario-1 reinfircement learning
scenario-2 supervised learning
scenario-3 unsupervised learning

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