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Machine Learning
Lunch & Learn - Session 2
Luis Borbon
29/06/2017
Table of contents
1. Recap
2. Learning Skills
3. Data Science Fields
4. Languages
5. Tools and Technologies
6. Linear regression
Recap
Recap
Learning Skills
Learning Skills Pyramid
Data Science Fields
Data Science Fields
Languages
The most popular tools on Kaggle
Job Postings
Tools and Technologies
Technologies
Tools
Toolkit
Scikit-learn
Methods - Linear regression
Types of data
Numerical data. These data have meaning as a measurement, such as a person’s height, weight, IQ, or
blood pressure; or they’re a count, such as the number of stock shares a person owns.
Discrete data represent items that can be counted; they take on possible values that can be
listed out. The list of possible values may be fixed (also called finite); or it may go from 0, 1, 2, on to infinity
(making it countably infinite).
Continuous data represent measurements; their possible values cannot be counted and can
only be described using intervals on the real number line.
Categorical data: Categorical data represent characteristics such as a person’s gender, marital status,
hometown, or the types of movies they like.
Types of Statistical Data
Dependent and independent variables
In mathematical modeling, statistical modeling and experimental sciences,
there are dependent and independent variables.
The models or experiments investigate how the former depend on the latter.
The dependent variables represent the output or outcome whose variation is
being studied.
The independent variables represent inputs or causes.
Linear regression
Statistics
In an experiment, the dependent variable is the event
expected to change when the independent variable is
manipulated.
In data mining tools (for multivariate statistics and machine
learning), the depending variable is assigned a role as target
variable (or in some tools as label attribute), while a
dependent variable may be assigned a role as regular
variable.
Known values for the target variable are provided for the
training data set and test data set, but should be predicted for
other data. The target variable is used in supervised learning
algorithms but not in non-supervised learning.
Linear regression
Linear regression

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Machine learning - session 2

  • 1. Machine Learning Lunch & Learn - Session 2 Luis Borbon 29/06/2017
  • 2. Table of contents 1. Recap 2. Learning Skills 3. Data Science Fields 4. Languages 5. Tools and Technologies 6. Linear regression
  • 10. The most popular tools on Kaggle
  • 14. Tools
  • 17.
  • 18. Methods - Linear regression
  • 19. Types of data Numerical data. These data have meaning as a measurement, such as a person’s height, weight, IQ, or blood pressure; or they’re a count, such as the number of stock shares a person owns. Discrete data represent items that can be counted; they take on possible values that can be listed out. The list of possible values may be fixed (also called finite); or it may go from 0, 1, 2, on to infinity (making it countably infinite). Continuous data represent measurements; their possible values cannot be counted and can only be described using intervals on the real number line. Categorical data: Categorical data represent characteristics such as a person’s gender, marital status, hometown, or the types of movies they like.
  • 21. Dependent and independent variables In mathematical modeling, statistical modeling and experimental sciences, there are dependent and independent variables. The models or experiments investigate how the former depend on the latter. The dependent variables represent the output or outcome whose variation is being studied. The independent variables represent inputs or causes.
  • 22. Linear regression Statistics In an experiment, the dependent variable is the event expected to change when the independent variable is manipulated. In data mining tools (for multivariate statistics and machine learning), the depending variable is assigned a role as target variable (or in some tools as label attribute), while a dependent variable may be assigned a role as regular variable. Known values for the target variable are provided for the training data set and test data set, but should be predicted for other data. The target variable is used in supervised learning algorithms but not in non-supervised learning.

Editor's Notes

  1. google/grumpy (python to go)
  2. Dependent and independent variables
  3. Statistics In an experiment, the dependent variable is the event expected to change when the independent variable is manipulated.[8] In data mining tools (for multivariate statistics and machine learning), the depending variable is assigned a role as target variable (or in some tools as label attribute), while a dependent variable may be assigned a role as regular variable.[9] Known values for the target variable are provided for the training data set and test data set, but should be predicted for other data. The target variable is used in supervised learning algorithms but not in non-supervised learning.