Machine Learning with R Training at Massive Tech – During this training you will know about the basics of Machine learning using an approachable, and well-known, programming language. You’ll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning and do a comparison of each.
Massive Tech brings you a comprehensive course that will help you go from basic to advanced concepts in Machine Learning using R, that language that was built by statisticians, for statisticians. Learn to build systems that learn from experience, and exploit data to create simple predictive models of the world.
Machine Learning is immensely exciting and creative, and those who have a deep understanding of this smart technology are well equipped to embark on one of the most lucrative careers of this age. Get started on creating innovation that is powered by new-age thinking: become a part of the Machine Learning revolution today!
Prerequisites
- Programming Knowledge is an added advantage.
- Familiarity with statistics
Key Features
- LIVE Instructor-led Classes
- 24x7 on-demand technical support for assignments, queries, quizzes, project, etc.
- Flexibility to attend the class at your convenient time.
- Server Access to Massive's Tech Management System until you get into your dream carrier.
- A huge database of Interview Questions
- Professional Resume Preparation
- Earn a Skill Certificate
- Enroll today and get the advantage.
Curriculum
- Statistical analysis concepts
- Descriptive Statistics
- Introduction to probability and Bayes theorem
- Probability distribution
- Hypothesis testing & scores
- Introduction to R Programming
- Installing and Loading Libraries
- Data Structures in R
- Control & Loop Statements in R
- Functions in R
- Loop Functions in R
- Strings Manipulation & Regular Expression in R
- Working with Data in R
- Data Visualization in R
- Machine Learning Modelling Flow
- Types of Machine Learning
- Performance Measures
- Bias-Variance Trade-Off
- Overfitting &Underfitting
- How to treat Data in ML
- Maxima and Minima
- Cost Function
- Learning Rate
- Optimization Techniques
- Linear Regression
- Logistic Regression
- K-NN Classification
- Naïve Bayesian Classifiers
- SVM-Support Vector Machines
- Clustering approaches
- K Means clustering
- Hierarchical clustering
- Decision Trees
- Introduction to Ensemble Learning
- Different Ensemble Learning Techniques
- Bagging
- Boosting
- Random Forests
- PCA(Principle Component Analysis) and Its applications
- Introduction to Recommendation Systems
- Types of Recommendation Techniques
- Collaborative Filtering
- Content based Filtering
- Hybrid RS
- Performance measurement
Have Any Questions?
We are happy to answer any questions and we appreciate every feedback about our work!