Machine Learning For Absolute Beginners 2020 | Machine Learning Tutorial For Beginners | Simplilearn

Machine Learning For Absolute Beginners 2020 | Machine Learning Tutorial For Beginners | Simplilearn

This Machine Learning tutorial video is designed for beginners to learn Machine Learning from scratch. You will learn, what is Machine Learning, 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 and finally implement a Machine hands-on demo on Linear Regression Algorithm using Python.

Free Machine Learning Course:

Below topics are explained in this Machine Learning tutorial:
00:00 life without machine learning
02:09 life with machine learning
04:12 What is machine learning
05:49 Types of machine learning
10:12 The right machine learning solutions
13:10 Machine learning Algorithms
31:34 Use Case

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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 knowledge and hands-on skills required for certification and job competency in Machine Learning.

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.
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

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