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.

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
  • Read/write csv, excel, json, sql, html, etc
• NumPy
  • Scientific computing
• Matplotlib
  • Plotting library
• scikit-learn
  • Machine learning library which offers many supervised and unsupervised algorithms.
  • Interactive cheet-sheet

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()