In this section, we take the popular ML terms and review them. This non-exhaustive review will helps us as a quick refresher and enable us to follow the projects covered by this book without any hiccups.
ML terminology – a quick review
by Dr. Sunil Kumar Chinnamgari, Cory Lesmeister
Advanced Machine Learning with R
- Title Page
- Copyright and Credits
- About Packt
- Contributors
- Preface
- Preparing and Understanding Data
- Linear Regression
- Logistic Regression
- Advanced Feature Selection in Linear Models
- K-Nearest Neighbors and Support Vector Machines
- Tree-Based Classification
- Neural Networks and Deep Learning
- Creating Ensembles and Multiclass Methods
- Cluster Analysis
- Principal Component Analysis
- Association Analysis
- Time Series and Causality
- Text Mining
- Exploring the Machine Learning Landscape
- ML versus software engineering
- Types of ML methods
- ML terminology – a quick review
- Deep learning
- Big data
- Natural language processing
- Computer vision
- Cost function
- Model accuracy
- Confusion matrix
- Predictor variables
- Response variable
- Dimensionality reduction
- Class imbalance problem
- Model bias and variance
- Underfitting and overfitting
- Data preprocessing
- Holdout sample
- Hyperparameter tuning
- Performance metrics
- Feature engineering
- Model interpretability
- ML project pipeline
- Learning paradigm
- Datasets
- Summary
- Predicting Employee Attrition Using Ensemble Models
- Implementing a Jokes Recommendation Engine
- Fundamental aspects of recommendation engines
- Getting started
- Understanding the Jokes recommendation problem and the dataset
- Building a recommendation system with an item-based collaborative filtering technique
- Building a recommendation system with a user-based collaborative filtering technique
- Building a recommendation system based on an association-rule mining technique
- Content-based recommendation engine
- Building a hybrid recommendation system for Jokes recommendations
- Summary
- References
- Sentiment Analysis of Amazon Reviews with NLP
- The sentiment analysis problem
- Getting started
- Understanding the Amazon reviews dataset
- Building a text sentiment classifier with the BoW approach
- Understanding word embedding
- Building a text sentiment classifier with pretrained word2vec word embedding based on Reuters news corpus
- Building a text sentiment classifier with GloVe word embedding
- Building a text sentiment classifier with fastText
- Summary
- Customer Segmentation Using Wholesale Data
- Understanding customer segmentation
- Understanding the wholesale customer dataset and the segmentation problem
- Identifying the customer segments in wholesale customer data using k-means clustering
- Identifying the customer segments in the wholesale customer data using DIANA
- Identifying the customer segments in the wholesale customers data using AGNES
- Summary
- Image Recognition Using Deep Neural Networks
- Credit Card Fraud Detection Using Autoencoders
- Automatic Prose Generation with Recurrent Neural Networks
- Winning the Casino Slot Machines with Reinforcement Learning
- Creating a Package
- Other Books You May Enjoy
ML terminology – a quick review
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