Predictive Modeling with Python
This course is part of Applied Data Analytics.
This comprehensive course delivers practical training in statistical analysis and machine learning with Python, focusing on real-world applications of predictive modeling. Students will gain proficiency in managing and preprocessing diverse data types, conducting hypothesis testing using both parametric and non-parametric statistical methods, and building exploratory data analysis (EDA) models to uncover meaningful insights. The curriculum covers essential probability distributions, inferential statistics, and advanced machine learning techniques for regression and classification. Participants will learn to evaluate model performance, optimize algorithms through hyperparameter tuning, and implement feature engineering to enhance predictive capabilities. Through hands-on projects and practical exercises, learners will develop the analytical skills needed to transform raw data into accurate predictive models that support data-driven decision-making across various industries.
Instructors:
English
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What you'll learn
Manage and preprocess different types of data for statistical analysis
Apply appropriate probability distributions to model various data scenarios
Conduct hypothesis testing using both parametric and non-parametric methods
Implement exploratory data analysis techniques to uncover patterns in complex datasets
Build regression and classification models for predictive analytics
Evaluate model performance using appropriate metrics and validation techniques
Skills you'll gain
This course includes:
9.7 Hours PreRecorded video
23 assignments
Access on Mobile, Tablet, Desktop
Batch access
Shareable certificate
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There are 6 modules in this course
This course provides a structured approach to predictive modeling with Python, covering both statistical foundations and machine learning applications. The curriculum begins with fundamental data concepts, teaching students to recognize different data types and apply appropriate statistical measures. It then progresses to probability distribution functions, where learners apply various distributions to model different types of data. The third module focuses on inferential statistics, including sampling techniques, hypothesis testing, and both parametric and non-parametric methods. Students then explore exploratory data analysis (EDA), learning to clean data, handle missing values, and perform feature engineering. The fifth module introduces predictive modeling algorithms, including regression and classification techniques, with emphasis on model evaluation and optimization. Throughout the course, practical demonstrations and hands-on exercises reinforce theoretical concepts, preparing students to apply these techniques to real-world scenarios.
Data and Information
Module 1 · 1 Hours to complete
Probability Distribution Function
Module 2 · 2 Hours to complete
Inferential Statistics
Module 3 · 3 Hours to complete
Introduction to (Exploratory Data Analysis) EDA
Module 4 · 3 Hours to complete
Predictive Modeling and Analysis
Module 5 · 4 Hours to complete
Course Wrap-Up and Assessment
Module 6 · 1 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Applied Data Analytics
Instructor
Inspiring the Next Generation of Tech Professionals
Edureka is dedicated to providing high-quality, instructor-led online training, empowering professionals to enhance their skills in various domains. The platform features a diverse team of experienced instructors who are passionate about teaching and possess extensive industry knowledge. These instructors facilitate a wide range of courses covering topics such as data science, artificial intelligence, machine learning, and cloud computing. Edureka's commitment to education is reflected in its innovative approach to learning, which includes interactive sessions, real-world projects, and 24/7 support for students. By fostering a collaborative learning environment, Edureka ensures that learners not only acquire technical skills but also develop critical thinking and problem-solving abilities essential for success in today's fast-paced job market.
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Frequently asked questions
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