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Machine-Learning-A-Z

As part of #100DaysofMLCode Challenge, I have started to strengthen my foundation in Machine Learning with the course "Machine learning A-Z" on Udemy.

Topics covered so far:

REGRESSION:

• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Support Vector Regression
• Decision Tree Regression
• Random Forest Regression

CLASSIFICATION:

• Logistic Regression
• K-Nearest Neighbor
• Support Vector Machines
• Kernel SVM
• Naive Bayes
• Decision Tree
• Random Forest

CLUSTERING:

• K-Means
• Hierarchical

ASSOCIATION RULE LEARNING

• Apriori
• Eclat

REINFORCEMENT LEARNING

• Upper Confidence Bound
• Thompson Sampling

NATURAL LANGUAGE PROCESSING

• Bag of words model

DEEP LEARNING

• Artificial Neural Networks
• Convolutional Neural Networks

DIMENSIONALITY REDUCTION

• Principal Component Analysis
• Linear Discrimant Analysis
• Kernel PCA