Machine Learning is the most debated technology of the 21st century. In this machine learning crash course, you will learn about the basics of machine learning and the various applications of machine learning. You will understand the different machine learning algorithms with hands-on demo in Python and get an idea about a machine learning engineer's salary, roles, and responsibilities.

🔥Free Machine Learning Course: https://www.simplilearn.com/learn-machine-learning-basics-skillup?utm_campaign=MachineLearningCrashCourse&utm_medium=Description&utm_source=youtube

Below topics are explained in this Machine Learning tutorial:
00:00:00 Basics of machine learning python
00:08:42 Machine learning python vs Deep learning vs Artificial Intelligence
00:12:10 Human vs Artificial Intelligence
00:14:06 What is Machine learning python and Deep Learning all about?
00:26:22 Types of AI and Machine Learning
00:35:12 Application of machine learning
00:40:06 Machine learning Algorithms Classification
01:25:33 what is Clustering
01:26:19 K-means Clustering
01:55:55 Logistic Regression
02:21:26 what is a random forest
02:30:08 How does Random forest work?
03:00:07 Naive Bayes
03:42:35 Machine learning Engineer salary, Roles, and responsibilities
03:42:40 Who is a machine learning engineer
03:43:40 Responsibilities of a Machine Learning Engineer
03:48:03 Sills of a Machine Learning Engineer
03:54:06 Resume of a Machine Learning Engineer

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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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