### What is Machine Learning?

Machine learning is a method of teaching computers to learn from data, without being explicitly programmed.

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- Machine Learning 101 (30)
- Statistics 101 (38)
- Supervised Learning (114)
- Regression (42)
- Classification (46)
- Logistic Regression (10)
- Support Vector Machine (10)
- Naive Bayes (4)
- Discriminant Analysis (5)
- Classification Evaluations (9)

- Classification & Regression Trees (CART) (23)

- Unsupervised Learning (55)
- Clustering (28)
- Distance Measures (9)
- Dimensionality Reduction (9)

- Deep Learning (23)
- Data Preparation (34)
- General (5)
- Standardization (6)
- Missing data (7)
- Textual Data (16)

Machine learning is a method of teaching computers to learn from data, without being explicitly programmed.

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Learning from training data to make predictions about new data point is Supervised Learning.

Identification of hidden patterns and associations from data is unsupervised learning.

Both discriminative and generative models have the ability to learn model parameters in a classification setting, but they are based on entirely different mechanisms and serve different purposes.

Linear models are a class of models in which a response variable is linearly related to one or more predictors.

Many non-linear relationships can be transformed into linear relationships through logarithmic and power transformations

Structured Data is data that has a clear and pre-defined schema. Unstructured Data encompasses the wide spectrum of data that does not fall within the structured category.

Feature engineering is the process of deriving informative input attributes that can be passed to a machine learning algorithm in order to learn their associations with the target variable.

The bias/variance tradeoff refers to the challenge of finding a model that both performs at a high level of accuracy on the data on which it is trained (bias) while at the same time generalizes well to unseen data (variance)

Predicting a continuous numerical value (ex: wage, selling price, etc.) is Regression.

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