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Feature Engineering for Machine Learning
4.47
Beginner level
1.5 learning hrs
About this course
Feature Engineering is to extract the features from the data and substitute them in the algorithms.This course will help you to understand the concept of upsampling, downsampling, SMOTE, etc. Also, the course will help you understand to build the model using logistic regression and make you understand the confusion matrix.
Course outline
Get access to the complete curriculum once you enroll in the course
4.47
United Arab Emirates
“Insightful and Well-Structured Course”
I enjoyed the detailed curriculum and the instructor's teaching approach. The quizzes and assignments were challenging and helped solidify my understanding of feature engineering concepts. The course is easy to follow and perfect for anyone looking to dive deeper into this topic. Highly recommended!
“Comprehensive guide to feature engineering and model tuning.”
I enjoyed the thorough explanation of feature engineering and how it relates to improving model accuracy. The course also did a great job of covering model tuning with practical examples. I would have liked more exercises using diverse datasets to deepen my understanding further.
Abdul Karim Abdul Qadir Ghulam Haidar
“Good and very informative course helped me a lots”
The course was incredibly helpful in introducing me to the basics of machine learning. I particularly enjoyed the hands-on projects, which allowed me to practice key concepts like model evaluation and feature engineering. The content was well-structured, although I would have liked a deeper dive into hyperparameter tuning and model optimization. The instructor was clear and engaging, making complex topics accessible. I feel more confident in applying machine learning techniques to real-world problems, and I look forward to building more projects in the future
Kashif Ahmed
“Skills enhancing course and such a great opportunity”
An introduction to feature engineering course provides foundational skills crucial for transforming raw data into meaningful inputs for machine learning models. By learning how to select, modify, and create features from data, participants gain the ability to improve model accuracy and performance. The course covers techniques for handling various data types, dealing with missing values, and extracting relevant patterns, which enhances the effectiveness of predictive analytics and data-driven decision-making. With these skills, individuals can optimize model training processes, reduce overfitting, and ultimately deliver more insightful and actionable results, making it a valuable asset for data scientists and analysts across diverse industries.
“skillfull,understandable and amaizing”
The topic was apprehensive and amazing and i got some skills
“Introduction to Feature Engineering Course”
This is a fantastic course on feature engineering and its techniques, including oversampling and undersampling. Thank you!
Malaysia
“Feature engineering in machine learning”
Well- engineered capabilities can mitigate noise, outliers, and missing values in the information, resulting in more reliable and correct model predictions.
Dimensionality Reduction: Strategic feature selection and extraction lessen the dimensionality of data, which not only accelerates computations but also prevents overfitting and improves model generalization
By creating features that reflect domain-specific insights, feature engineering amplifies the predictive relevance of attributes, ensuring models are aligned with the problem.
“Great Learning Experience, easy to follow.”
I really enjoyed this introduction to Feature engineering course and thought it was exceptionally well-designed. The instructors did a great job of starting from the very fundamentals of feature engineering and gradually building up my understanding. The lessons progressed in a logical, step-by-step manner that made it easy to follow along and apply what I was learning.
“Gained Practical Skills in Data Science Projects”
I enjoyed working on real-world data science projects like customer churn prediction. It helped me apply concepts like data preprocessing, feature engineering, and classification models. The hands-on experience with tools such as Python and Scikit-learn deepened my understanding and boosted my confidence.
India
“Introduction to Feature Engineering”
Introduction, Undersampling, Oversampling and Hands-On-Exercise on Tuning the Model.
What our learners enjoyed the most
Instructor
70%
70% of learners liked instructor's teaching style
Skill & tools
63%
63% of learners found all the desired skills & tools
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