Employee Attrition Prediction Using Machine Learning
A brief overview of the material covered in this course is that it teaches students how to use logistic regression and the XG-Boost algorithm in machine learning to forecast employee turnover.
Description for Employee Attrition Prediction Using Machine Learning
Development of an Attrition Prediction Model: Develop a machine learning model to forecast employee turnover based on variables such as job satisfaction, commuting distance, remuneration, and performance metrics.
Exploration of Machine Learning Algorithms: Acquire proficiency in two machine learning algorithms: logistic regression classifier and Extreme Gradient Boosted Trees (XGBoost).
Human Resources Software: Acquire the ability to implement predictive models in Human Resources to identify personnel at risk of attrition.
Hands-On Project-Based Learning: Participate in hands-on experience by constructing, training, and evaluating the model using real-world data attributes.
Level: Beginner/Intermediate/ Advanced
Certification Degree: Yes
Languages the Course is Available: 1
Offered by: On Coursera provided by Coursera Project Network
Duration: 2 hours
Schedule: Hands-on learning
Pricing for Employee Attrition Prediction Using Machine Learning
Use Cases for Employee Attrition Prediction Using Machine Learning
FAQs for Employee Attrition Prediction Using Machine Learning
Reviews for Employee Attrition Prediction Using Machine Learning
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Employee Attrition Prediction Using Machine Learning
Gain proficiency in the automation of software development processes through the utilization of generative artificial intelligence, AI-assisted programming, MLOps, and Amazon Web Services.
Discover AI terminology, ethical norms, and protocols for responsibly utilizing and citing Generative AI.
Examine how to improve learning and preserve integrity by incorporating morally sound and useful AI tools into evaluation procedures.
Acquire practical expertise in the integration of machine learning models into pipelines, optimizing performance, and efficiently managing versioning and artifacts.
Learn how to use AI technologies for personal development and active learning, embrace continuous learning, and cultivate a growth mindset.
Understand foundational knowledge of AI and RegTech, their societal implications, and the discourse around their future integration and obstacles.
This training provides professionals with knowledge and practical advice on AI ethics, compliance issues, and risk management.
Gain extensive knowledge in AI technologies relevant to digital marketing, involving precise data analysis, content creation, and tools for optimizing social media and consumer segmentation.
Study the ethical consequences of AI development and implementation, emphasizing generative AI, AI governance, and pragmatic ethical decision-making in practical contexts.
The material equips data engineers to incorporate machine learning models into pipelines while adhering to best practices in collaboration, version control, and artifact management.
Featured Tools
Explore the world of AI-powered language processing by acquiring the skills necessary to construct chatbots, analyze sentiment, and incorporate AI insights into practical applications.
Discover AI terminology, ethical norms, and protocols for responsibly utilizing and citing Generative AI.
This course covers Python programming, TensorFlow for linear regression, and app development for stock market prediction.
Examine how to improve learning and preserve integrity by incorporating morally sound and useful AI tools into evaluation procedures.
To address OpenAI Gym challenges and real-world problems, this course offers pragmatic artificial intelligence methods like Genetic Algorithms, Q-Learning, and neural network implementation.
Compare & Review AI Tool Directory AI Course Marketplace
