Mastering Feature Engineering Essentials

Categories Data Science

Mastering Feature Engineering Essentials

0(0 Ratings)

Level

Intermediate
Duration

8 hours
Last Updated

February 9, 2024
Certificate

Certificate of completion

Unlock the potential of feature engineering with our comprehensive course. Dive deep into essential techniques for extracting, transforming, and selecting features to enhance machine learning models' performance. Explore advanced methodologies for handling categorical, numerical, and text data effectively. Gain hands-on experience in designing feature pipelines and optimizing model inputs, empowering you to master feature engineering essentials.

Optimize model performance through targeted feature selection, boosting accuracy and efficiency of predictive algorithms.
Enhance data interpretability by transforming raw inputs into meaningful, informative features, facilitating clearer insights.
Mitigate overfitting risks by crafting robust features that capture essential patterns while minimizing noise.
Accelerate innovation by mastering advanced techniques to engineer features tailored to diverse machine learning tasks.

Course Curriculum

Anomaly Detection
Anomaly detection is one of the most important tasks in data science. It is used in many critical applications such as transaction monitoring, manufacturing, fraud detection, intrusion detection, etc. Generally, this is considered an unsupervised machine learning approach as the task is to detect anomalous patterns in the data without having any known labels. This course covers anomaly detection in detail where the attendees will get a complete understanding of anomalies in data, the methods used to detect anomalies and hands-on implementation of anomaly detection in python. Along with the understanding of these key concepts, the attendees will also get the python codes used in this course to practice the implementations in real-time. In the end, the attendees will also get a certificate for successfully completing this course.

What are the anomalies?
Methods for Anomaly Detection
Isolation Forest
Local Outlier Factor
Hands-on implementation of Anomaly Detection
Video Tutorial on Anomaly Detection

06:44
Hands-on implementations explained

03:50
Codes for Practice

Principal Component Analysis

Categorical Encoding

Linear Discriminant Analysis

Kernel PCA

t-SNE

Outlier Treatment

Feature Selection Techniques

Self Assessment

Testimonials

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A course by

Association of Data Scientists

Ankita B

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Mastering Feature Engineering Essentials

What I will learn?

Course Curriculum

What are the anomalies?

Methods for Anomaly Detection

Isolation Forest

Local Outlier Factor

Hands-on implementation of Anomaly Detection

Video Tutorial on Anomaly Detection

Hands-on implementations explained

Codes for Practice

Principal Component Analysis

What is Principal Component Analysis?

How Does PCA Work?

Benefits and limitations of PCA

Hands-on implementation of PCA in Python

Video tutorial on Principal Component Analysis

Hands-on implementations Explained

Codes For Practice

Categorical Encoding

About Categorical Encoding

Different Approches to Categorical Encoding

Hands-on Implementation of Categorical Encoding Techniques

Video Tutorial on Categorical Encoding

Hands-on implementations explained

Codes for Practice

Linear Discriminant Analysis

Linear Discriminant Analysis

How does LDA work?

Steps of LDA

Difference between Principal Component Analysis and Linear Discriminant Analysis

Video Tutorial on Linear Discriminant Analysis

Linear Discriminant Analysis in Python

Video Tutorial on Implementing LDA in Python

Code for Practice

Kernel PCA

What is Principal Component Analysis?

Introduction to Kernel-PCA

How does Kernel PCA work?

Video Tutorial on Kernel-PCA

Kernel-PCA in Python

Video Tutorial on Implementing K-PCA in Python

Code for Practice

t-SNE

What is a t-SNE?

Workings of t-SNE

Steps involved in t-SNE

PCA vs t-SNE

Video Tutorial on t-SNE

t-SNE Implementation in Python

Implementation Video Tutorial on t-SNE in Python

Code for practice

Outlier Treatment

Outliers and Outliers Treatment

Feature Selection Techniques

Feature Selection Techniques

Video Tutorial on Feature Selection Techniques

Feature Selection Techniques in Python

Feature Selection Techniques Implementation Video

Practice Feature Selection in Python

Self Assessment

Quiz

Testimonials

A course by

Material Includes

Requirements

Who Should Take this course?

See how employees at top companies are mastering Artificial Intelligence skills

Chartered Data Scientist (CDS™)

The highest distinction in the data science profession. Not just earn a charter, but use it as a designation.

Lattice - Our ML Journal

Lattice is an international peer-reviewed and refereed journal on machine learning hosted and managed by ADaSci

Become ADaSci Member

We offer both Individual & Institutional Membership.

Get exclusive access to leading AI Resourses

The power of intelligence to propel science and make a difference

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CDS Program

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