\(\color{red}{\text{ Draft: Work in Progress..... }}\)

### Learning Styles:

- Supervised Learning
- The values of target variable, in training set are known.

- Unsupervised Learning
- data is not labeled and outcomes are unknown.
- Seek to find hidden structure in unlabeled data
- Example: Clustering algorithms

- Semi-supervised Learning
- Uses both labeled and unlabeled data to improve supervised learning.
- Example: in Deep learning Generative models

### Problems:

- Classification problem: The target variable is qualitative (categorical/nominal)
- Binary- Two possible outcomes only
- e.g. Yes/No, 0/1

- Multi-Class- More than 2 outcomes
- e.g. Positive, Neutral or Negative customer sentiment/feedback

- Binary- Two possible outcomes only
- Regression problem: The target is quantitative (discrete or continuous)
- Can take any real number in real line
- e.g. Stock prices, estimated revenue, interest rates, price of house, Number of accidents

- Can take any real number in real line
- Others:
- Clustering
- Dimension reduction

```
import pandas as pd
#from sklearn.cross_validation import train_test_split, StratifiedKFold
from sklearn.ensemble import GradientBoostingClassifier as GBC, RandomForestClassifier
from sklearn.model_selection import GridSearchCV
```

#### 1. Linear Regression:

#### 2. Logistic Regression

#### 3. Other regression problems

#### 4. Classification and Regression Trees (Decision Tree)

#### 5. Naive Bayes

#### 6. Ensemble Models

Random Forest

- Boosted Trees
- Adaptive boosting
- Gradient Boosting
- Gradient boost
- XGBoost
- Catboost