RF, Adabost.M1, DecisionTree, Logistic Regression , Naive Bayes, Neural Network, CNN, K-mean , Linear regress
Master key concepts and practical skills through structured learning modules. By completing this curriculum, you'll gain valuable expertise applicable to real-world scenarios.
At the end this course, A student will be able to do the following: For continuous data , you will be able to Train a linear regression model , select the best linear model for a given data and predict. For categorical data (Binary classification task ), you will be able to train models such as logistic regression (LR), Decision Tree (DT), Neural Network (NN), Convolutional Neural Network (CNN or ConVnet) , AdaBoost.M1, Random Forest (RF) and Naïve Bayes (NB) . You will be able to combine models to better your prediction. For clustering task, out of this class, a student will be able to implement the K-mean clustering which is the widely used clustering algorithm .
This comprehensive Machine learning with R (RF, Adabost.M1, DT, NB, LR, NN) curriculum is designed to take you from foundational concepts to advanced implementation. Each module builds upon the previous, ensuring a structured learning path that maximizes knowledge retention and practical application.
Understand what you need to succeed in this course and determine if it's the right fit for your learning goals
What you need before starting this Machine learning with R (RF, Adabost.M1, DT, NB, LR, NN) course:
This course is perfect for:
Everything you need to know about this online course, from duration to certification
Course Instructor
Modeste Atsague
Expert instructor with industry experience
Course Language
English
All materials in English
This online course offers comprehensive training with expert instruction, practical exercises, and a certificate of completion. Join thousands of students advancing their careers through quality online education.
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