Data pre-processing for Machine Learning in Python
Data pre-processing for Machine Learning in Python, available at $79.99, has an average rating of 4.3, with 48 lectures, based on 96 reviews, and has 1835 subscribers.
You will learn about How to fill the missings in numerical and categorical variables How to encode the categorical variables How to transform the numerical variables How to scale the numerical variables Principal Component Analysis and how to use it How to apply oversampling using SMOTE How to use several useful objects in scikit-learn library This course is ideal for individuals who are Python developers or Aspiring data scientists or People interested in machine learning and artificial intelligence It is particularly useful for Python developers or Aspiring data scientists or People interested in machine learning and artificial intelligence.
Enroll now: Data pre-processing for Machine Learning in Python
Summary
Title: Data pre-processing for Machine Learning in Python
Price: $79.99
Average Rating: 4.3
Number of Lectures: 48
Number of Published Lectures: 48
Number of Curriculum Items: 48
Number of Published Curriculum Objects: 48
Original Price: $29.99
Quality Status: approved
Status: Live
What You Will Learn
- How to fill the missings in numerical and categorical variables
- How to encode the categorical variables
- How to transform the numerical variables
- How to scale the numerical variables
- Principal Component Analysis and how to use it
- How to apply oversampling using SMOTE
- How to use several useful objects in scikit-learn library
Who Should Attend
- Python developers
- Aspiring data scientists
- People interested in machine learning and artificial intelligence
Target Audiences
- Python developers
- Aspiring data scientists
- People interested in machine learning and artificial intelligence
In this course, we are going to focus on pre-processing techniques for machine learning.
Pre-processing is the set of manipulations that transforma raw dataset to make it used by a machine learning model. It is necessary for making our data suitablefor some machine learning models, to reduce the dimensionality,to better identify the relevant data,and to increase model performance. It’s the most important part of a machine learning pipeline and it’s strongly able to affect the success of a project. In fact, if we don’t feed a machine learning model with the correctly shaped data, it won’t work at all.
Sometimes, aspiring Data Scientists start studying neural networks and other complex models and forget to study how to manipulate a datasetin order to make it used by their algorithms. So, they fail in creating good models and only at the end they realize that good pre-processing would make them save a lot of time and increase the performanceof their algorithms. So, handling pre-processing techniques is a very important skill. That’s why I have created an entire coursethat focuses only on data pre-processing.
With this course, you are going to learn:
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Data cleaning
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Encoding of the categorical variables
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Transformation of the numerical features
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Scikit-learn Pipeline and ColumnTransformer objects
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Scaling of the numerical features
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Principal Component Analysis
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Filter-based feature selection
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Oversampling using SMOTE
All the examples will be given using Python programming language and its powerful scikit-learn library. The environment that will be used is Jupyter, which is a standard in the data science industry. All the sections of this course end with some practical exercisesand the Jupyter notebooks are all downloadable.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction to the course
Lecture 2: Numerical and categorical variables
Lecture 3: The dataset
Lecture 4: Required Python packages
Lecture 5: Jupyter notebooks
Chapter 2: Data cleaning
Lecture 1: Introduction to data cleaning
Lecture 2: Selecting numerical and categorical variables
Lecture 3: Cleaning the numerical features
Lecture 4: Cleaning the categorical features
Lecture 5: KNN blank filling
Lecture 6: ColumnTransformer and make_column_selector
Lecture 7: Exercises
Chapter 3: Encoding of the categorical features
Lecture 1: Introduction to the encoding of categorical variables
Lecture 2: One-hot encoding
Lecture 3: Ordinal encoding
Lecture 4: Label encoding of the target variable
Lecture 5: Exercise
Chapter 4: Transformations of the numerical features
Lecture 1: Introduction to transformations
Lecture 2: Power Transformation
Lecture 3: Binning
Lecture 4: Binarizing
Lecture 5: Applying an arbitrary transformation
Lecture 6: Exercise
Lecture 7: About power transformations
Chapter 5: Pipelines
Lecture 1: Define a transformation pipeline
Lecture 2: Pipelines and ColumnTransformer together
Lecture 3: Exercises
Chapter 6: Scaling
Lecture 1: Introduction to scaling
Lecture 2: Normalization, Standardization, Robust scaling
Lecture 3: Exercise
Chapter 7: Principal Component Analysis
Lecture 1: Introduction to PCA
Lecture 2: How to perform PCA
Lecture 3: Exercise
Lecture 4: A comment about scaling before PCA
Chapter 8: Filter-based feature selection
Lecture 1: Introduction to feature selection
Lecture 2: Numerical features, numerical target
Lecture 3: Numerical features, categorical target
Lecture 4: Categorical features, numerical target
Lecture 5: Categorical features, categorical target
Lecture 6: Feature importance according to a model
Lecture 7: A comment on mutual information
Lecture 8: A comment on feature selection with categorical variables
Lecture 9: Exercises
Chapter 9: A complete pipeline
Lecture 1: An example of a complete pipeline
Chapter 10: Oversampling
Lecture 1: Introduction to SMOTE
Lecture 2: How to perform SMOTE
Lecture 3: Exercise
Chapter 11: General guidelines
Lecture 1: Practical suggestions
Instructors
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Gianluca Malato
Your Data Teacher
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 1 votes
- 3 stars: 8 votes
- 4 stars: 28 votes
- 5 stars: 58 votes
Frequently Asked Questions
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You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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