R Machine Learning Projects: Implement supervised, unsupervised, and reinforcement learning techniques using R 3.5

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ASIN ‏ : ‎ B07KJDL5Y9
Publisher ‏ : ‎ Packt Publishing; 1st edition (January 14, 2019)
Publication date ‏ : ‎ January 14, 2019
Language ‏ : ‎ English
File size ‏ : ‎ 14410 KB
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 615 pages

Are you looking to enhance your machine learning skills using R 3.5? Look no further! In this post, we will explore various R machine learning projects that implement supervised, unsupervised, and reinforcement learning techniques.

Supervised Learning Projects:

1. Classification: Build a spam email classifier using the popular SpamAssassin dataset. Train a classification model to predict whether an email is spam or not based on its content.

2. Regression: Predict housing prices in a specific area using a dataset of housing features such as square footage, number of bedrooms, and location. Implement linear regression to build a predictive model.

Unsupervised Learning Projects:

1. Clustering: Use the famous Iris dataset to cluster different species of flowers based on their petal and sepal dimensions. Implement K-means clustering to group similar flowers together.

2. Dimensionality Reduction: Apply principal component analysis (PCA) to reduce the dimensionality of a dataset containing various features. Visualize the reduced dataset to identify patterns and relationships among the data points.

Reinforcement Learning Projects:

1. Q-Learning: Develop a Q-learning algorithm to train an agent to navigate a maze and reach a goal state. Implement reward-based learning to optimize the agent’s decision-making process.

2. Deep Q-Learning: Enhance the Q-learning algorithm by incorporating deep neural networks to handle complex environments and tasks. Train an agent to play a game like Atari’s Breakout using deep Q-learning.

These projects will provide you with hands-on experience in implementing different machine learning techniques using R 3.5. Whether you are a beginner or an experienced data scientist, these projects will help you sharpen your skills and expand your knowledge in the field of machine learning. Happy coding!
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