Machine Learning Tutorial | Machine Learning Basics | Machine Learning Algorithms | Simplilearn
This Machine Learning tutorial video is ideal for beginners to learn Machine Learning from scratch. By the end of this tutorial video, you will learn why Machine Learning is so important in our lives, what is Machine Learning, the various types of Machine Learning (Supervised, Unsupervised and Reinforcement learning), how do we choose the right Machine Learning solution, what are the different Machine Learning algorithms and how do they work (with simple examples and use-cases) and finally implement a Machine Learning project/ hands-on demo on Linear Regression Algorithm using Python.
This Machine Learning tutorial will cover the following topics:
1. Life without Machine Learning ( 01:06 )
2. Life with Machine Learning ( 02:29 )
3. What is Machine Learning ( 04:35 )
4. Machine Learning Process ( 05:27 )
5. Types of Machine Learning ( 06:14 )
6. Supervised Vs Unsupervised ( 09:32 )
7. The right Machine Learning solutions ( 10:35 )
8. Machine Learning Algorithms ( 13:33 )
9. Use case – Predicting the price of a house using Linear Regression ( 23:24 )
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
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You can also go through the Slides here: https://goo.gl/aNmKbQ
Machine Learning Articles: https://www.simplilearn.com/what-is-artificial-intelligence-and-why-ai-certification-article?utm_campaign=Machine-Learning-Tutorial-G7fPB4OHkys&utm_medium=Tutorials&utm_source=youtube
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To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Machine-Learning-Tutorial-G7fPB4OHkys&utm_medium=Tutorials&utm_source=youtube
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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.
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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.
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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 thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
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Who should take this Machine Learning Training Course?
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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Thanks one more time for the richful tutorial and the explanations provided.
I did the exercise consisting to predict the prices of the house from th 'boston housing' dataset. It works as explained in the video. I just have few questions concerning the training, test and prediction. Indeed, what guide the right choice of the train/test_size and random_state values? In my code I changed the test_size value to 0.5 and the random_state to 7, I got a nice prediction for arrays  &  but not for array for example! And when I compute the mean squared value, I get 33.36. Can such a value be acceptable? Thank you again for all.
Wow! very well put
Thanks for share 💛
GREAT, Thanks SL team
good information sir ..
its very useful to me
i must say the way you explained this topic , i m impressed thank you . simplilearn is really simple
Good teaching video. Can I have the dataset to practice on please? (firstname.lastname@example.org)