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

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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 and understand some vital machine learning algorithms, such as linear regression, logistic regression, decision trees, random forests, SVM, and hierarchical clustering techniques.

00:00:00 Machine Learning basics
00:14:25 R Programming
00:32:03 Machine Learning algorithms
05:51:25 Linear regression
06:20:16 Logistic regression
06:38:14 Decision Tree and Random forest
07:47:52 SVM - Support Vector Machine
08:06:56 Clustering
08:48:55Time Series Analysis

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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. These applications learn, grow, change, and develop when exposed to new data.

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 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 known data, the input cannot be guided to the algorithm, which is where the unsupervised term originates. This data is fed to the Machine Learning algorithm and is used to train the model. The trained model searches for a pattern and gives 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.

➡️ About Post Graduate Program In AI And Machine Learning
This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots.

✅ Key Features
- Post Graduate Program certificate and Alumni Association membership
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✅ Skills Covered
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- Generative AI
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- Generative Modeling
- Statistics
- Python
- Supervised Learning
- Unsupervised Learning
- NLP
- Neural Networks
- Computer Vision
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