Generative AI Crash Course for Non-Tech Professionals. Register Now >
Categories Data Science

Mastering Feature Engineering Essentials

  • Learn essential feature engineering techniques to optimize data for machine learning, from transforming variables to enhancing model performance, in this comprehensive and practical course.
0(0 Ratings)
2,093.00

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.
Show More

What I will learn?

  • 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

No Review Yet
No Review Yet

Material Includes

  • Self-paced study materials
  • Video Lessons
  • Hands-on codes
  • Self-assessment

Requirements

  • Learn essential feature engineering techniques to optimize data for machine learning, from transforming variables to enhancing model performance, in this comprehensive and practical course.

Who Should Take this course?

  • Beginners

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.