Course Information
Course Overview
kick start your machine learning journey with supervised learning for beginners, python, jupyter and scikit-learn!
If you are a developer, an architect, an engineer, a techie, an IT enthusiast, a student or just a curious person, if you are interested in taking on machine learning but you are not too sure where to start, this is probably the right course for you!!
In this course, we start with the basics and we explain the concept of supervised learning in depth, we also go over the various types of problems that can be solved using supervised learning techniques. Then we get more hands-on and illustrate some concepts relative to data preparation and model evaluation with bits of code that you can easily reuse. And last, we actually train and evaluate several models based on the most common machine learning algorithms for supervised learning such as K-nearest neighbors, logistic regression, decision trees and random forests.
I hope that you find this course fun and easy to follow and that it gives you the machine learning background you need to kick start your journey and be successful in this field!
Course Content
- 9 section(s)
- 23 lecture(s)
- Section 1 Introduction
- Section 2 Classification problems
- Section 3 Data analysis and preparation
- Section 4 Model testing and evaluation
- Section 5 Linear models
- Section 6 K nearest neighbors
- Section 7 Decision trees and random forests
- Section 8 Conclusion
- Section 9 Appendix
What You’ll Learn
- scikit-learn
- machine learning
- artificial intelligence
- jupyter
- python
- supervised learning
- regression
- classification
- data processing
- model training
- model evaluation
- hands-on experience
Skills covered in this course
Reviews
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HH.P.A.L Pathirana
things up to point and clear
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SShivansh Mishra
overall nice course, but more suitable to people having some experience exercises could be better and in depth
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PPrabhat Khetarpal
All the courses are however built on easy datasets which might not have some real world problems which we face, but the tutor does clear the ML concepts quite well with the practice dataset and by giving examples. Thus, I think its worth it to take up this course, only if you're very new to ML.
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SShehu Aminu Yaro
It is indeed