Exploratory Data Analysis (EDA)

Outliers And Their Treatment

Supervised Learning vs Unsupervised Learning

1. One hot encoding using dummy variables
2. One hot encoding using One hot encoder

1. Simple Linear regression
   1. Ordinary Least Squares
2. Multiple Linear regression
   1. Assumptions of Linear Regression
   2. Understanding the P-value
3. Polynomial Linear regression
4. Support Vector Regression
   1. Non-Linear SVR
5. Decision Tree Regression
6. Random Forest Regression
7. Ridge regression
8. Bias and Variance tradeoff
9. Lasso regression
10. Elasticnet regression

1. R-Squared Intuition
2. Adjusted R-Squared Intuition
3. Regression Model Selection

1. Logistic regression
2. Naive Bayes (Gaussian NB and Multinomial NB)
   1. Bayes Theorem
3. KNN Classifier (K-Nearest Neighbors)
4. SVM (Support Vector Machine)
5. Regularization
6. Kernel SVM
   1. Mapping to a higher dimension
   2. The Kernel Trick
   3. Types of Kernel Functions
   4. Non-Linear Kernel SVR
7. Decision TreeClassification
8. Random Forest Classification
9. Entropy
10. Gini Index

1. False Positives & False Negatives
2. Accuracy Paradox
3. CAP Curve
4. CAP Curve Aalsis
5. Classification Model Selection in Python

1. Supervised vs Unsupervised
2. K-Means Clustering Intuition
3. The Elbow Method
4. K-Means++
5. K-Means Clustering in Python

1. Hierarchical Clustering Intuition
2. How Dendrograms Work
3. Using Dendrograsms
4. Hierarchical Clustering in Python

1. Apriori Algorithm
2. Eclat

1. Upper Confidence Bound (UCB)
2. Thomson Sampling

1. High school mathematics level
2. Basic Python programming knowledge