Machine Learning Roadmap 2023 | Machine Learning Roadmap for Beginners | ML Roadmap | Simplilearn
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In this video, we are going to discuss *Machine Learning Roadmap 2023* which contains topics to be covered for a machine learning engineer, what is the role of a machine learning engineer, and the salary of a machine learning engineer which will help you to understand the steps you need to take up to become Machine Learning Engineer.
✅ 00:00 - Machine Learning Roadmap 2023
✅ 00:35 - What is Machine Learning?
This phase of the video contains the definition of machine learning.
✅ 01:07 - Computer Science for Machine Learning
This part of the video contains the role of computer science for a machine learning engineer.
✅ 01:30 - Programming languages and algorithms for Machine Learning
This part of the video has details about the programming language needed for a machine learning engineer.
✅02:58 - Mathematics and Statistics
This phase of the video is given why the fundamentals of maths and statistics are needed for a machine learning engineer.
✅03:30 - Conclusion
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Machine learning, in general, is a branch of artificial intelligence that uses data patterns to generate judgments or predictions. It allows computers to adapt to repeating processes and results based on past performance without requiring them to be explicitly programmed in advance, as with standard algorithms.
In simple English, it basically allows robots to learn from, predict, and adjust to prior behavior. It is a method of generating artificial intelligence without needing to predetermine all the guidelines and procedures. Now that we have learned the basics of machine learning, it’s time to focus on subjects that will help you to become a machine learning expert.
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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 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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