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Machine Learning A-Z: AI, Python & R, The machine learning process -…
Machine Learning A-Z: AI, Python & R
Clustering
K-means clustering
Hierarchical clustering
Introduction to Machine Learning
:
Applications of machine learning
Data Preprocessing
Exploratory data analysis
Feature engineering
Cleaning data
Splitting data into training and testing sets
Importing data
Association Rule Learning
Apriori algorithm
Eclat algorithm
Dimensionality Reduction
Linear discriminant analysis (LDA)
Kernel PCA
Principal component analysis (PCA)
Model Selection and Boosting
Model Selection
XGBoost
Regression
Polynomial regression
Support vector regression
Multiple linear regression
Decision tree regression
Simple linear regression
Random forest regression
Classification
Support vector machines
Naive Bayes
K-nearest neighbors
Decision tree classification
Logistic regression
Random forest classification
Deep Learning
Artificial neural networks
Convolutional neural networks
Applying Machine Learning to Real-World Problems
Natural Language Processing
Text classification
Bag-of-words model
Reinforcement Learning
Thompson Sampling
Upper Confidence Bound (UCB)
The machine learning process