Machine Learning With R Full Course | Machine Learning Course For Beginners

Machine Learning With R Full Course | Machine Learning Course For Beginners

In this Machine Learning with R full course video, you will understand the basics of machine learning and look at the various applications of Machine Learning. You will learn R programming in detail, understand some of the vital machine learning algorithms, such as linear regression, logistic regression, decision tree, random forest, SVM, and hierarchical clustering techniques. Finally, this video will help you understand the time series analysis in R.

In this Machine Learning with R full course video, you will understand the basics of machine learning and look at the various applications of Machine Learning. You will learn R programming in detail, understand some of the vital machine learning algorithms, such as linear regression, logistic regression, decision tree, random forest, SVM, and hierarchical clustering techniques. Finally, this video will help you understand the time series analysis in R.

  • Machine Learning basics
  • R Programming
  • Machine Learning algorithms
  • Linear regression
  • Logistic regression
  • Decision Tree and Random forest
  • SVM - Support Vector Machine
  • Clustering
  • Time Series Analysis

What Exactly is Machine Learning? A good start at a Machine Learning definition is that it is a core sub-area of Artificial Intelligence (AI). ML applications learn from experience (well data) like humans without direct programming. When exposed to new data, these applications learn, grow, change, and develop by themselves.

What is Supervised Learning? In supervised learning, we use known or labeled data for the training data. Since the data is known, the learning is, therefore, supervised, i.e., directed into successful execution. The input data goes through the Machine Learning algorithm and is used to train the model.

What is Unsupervised Learning? In unsupervised learning, the training data is unknown and unlabeled - meaning that no one has looked at the data before. Without the aspect of known data, the input cannot be guided to the algorithm, which is where the unsupervised term originates from. This data is fed to the Machine Learning algorithm and is used to train the model. The trained model tries to search for a pattern and give the desired response. In this case, it is often like the algorithm is trying to break code like the Enigma machine but without the human mind directly involved but rather a machine.

What is Reinforcement Learning? Like traditional types of data analysis, here, the algorithm discovers data through a process of trial and error and then decides what action results in higher rewards. Three major components make up reinforcement learning: the agent, the environment, and the actions. The agent is the learner or decision-maker, the environment includes everything that the agent interacts with, and the actions are what the agent does.

machine-learning r-programming developer data-science

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