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Predictive Modeling with Python
This course is part of Applied Data Analytics.
Course Cost
Free course
Intermediate
Skill Level
15 Hours
Self-paced lessons
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.
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
Optimize machine learning models through hyperparameter tuning and feature engineering
Apply statistical and machine learning methods to solve real-world business problems
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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Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
CREATED BY
PROVIDED BY
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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
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Faculties
These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.
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Frequently asked Questions
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