# Types of Machine learning
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## Supervised (input -> output)
### Regression (output is continuous)
* ==Linear Regression==
* Support Vector Machine (Support Vector Regression)
* K Nearest Neighbours
* Decision Tree
* Random Forest
### Classification (output is discrete)
* ==Logistic Regression==
* ==Naïve Bayes==
* Support Vector Machine (Support Vector Classification)
* K Nearest Neighbours
* Decision Tree
* Random Forest
## Unsupervised (learns patterns and structures from unlabeled data)
### Clustering
* K-Means
* Identify groups of data points that are similar to each other within the same cluster while being different from data points in other clusters.
### Dimension reduction
* Principal Component Analysis (PCA)
* Find the best way to tell a story using just a few important pictures, instead of showing every single detail and tons of words.
## Reinforcement (teaching a robot/dog to play a game: it learns by trying different things, getting rewards when it does well, and figuring out how to do better next time.)

## [Watch this video to review Supervised and Unsupervised models.](https://youtu.be/yN7ypxC7838?si=bdQkkpQH0B8I0AhF)

# Steps of Machine learning
* Data Collection
* Data Preparation (preprocessing)
* Handling missing values and data formats
* 
* Country name: Germany, GERMANY, germany, Deutschland, DE, De, de
* True/false: TRUE, true, True, FALSE, False, false, 1, 0
* Feature selection
* Dimensionality reduction
* Normalization
* Choice of Model
* Training of Model
* Remember to split the data into the training set and testing set
* Evaluation of Model
* Parameter Tuning and Optimization
* Predictions and Deployment

# Common Libraries
* [pandas](https://pandas.pydata.org/)
* Read/write csv, excel, json, sql, html, etc
* [NumPy](https://numpy.org/)
* Scientific computing
* [Matplotlib](https://matplotlib.org/)
* Plotting library
* [scikit-learn](https://scikit-learn.org/stable/)
* Machine learning library which offers many supervised and unsupervised algorithms.
* [Interactive cheet-sheet](https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html)

# Ways of importing libraries

# 1.
from sklearn.linear_model import LinearRegression
model = LinearRegression()

# 2.
from sklearn import linear_model
model = linear_model.LinearRegression()

# 3.
import sklearn
model = sklearn.linear_model.LinearRegression()

# 1.
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.svm import SVC

# 2.
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.svm import SVC

# 3.
import sklearn

dataset = sklearn.datasets.load_breast_cancer()
train_test_split = sklearn.model_selection.train_test_split()
cross_val_score = sklearn.model_selection.cross_val_score()
SVC = sklearn.svm.SVC()