Predictive Modeling with Python | CodeSignal Learn
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intermediate
intermediate
Predictive Modeling with Python
Machine Learning
5 courses
104 practices
13 hours
Dive into Predictive Modeling with Python, focusing on regression using the California Housing Dataset. Through hands-on coding, this path teaches you how to build and refine models. Master regression techniques and predictive modeling to make informed predictions.
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4.43
897 learners
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Verified skills you'll gain
Badge for Coding and Data Algorithms, Developing
DEVELOPING
Coding and Data Algorithms
Badge for Data Cleaning and Preprocessing, Developing
DEVELOPING
Data Cleaning and Preprocessing
Badge for Machine Learning Model Development, Intermediate
INTERMEDIATE
Machine Learning Model Development
Badge for Model Validation and Selection, Intermediate
INTERMEDIATE
Model Validation and Selection
Tools you'll use
MatPlotLib
Pandas
Python
Scikit-learn
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Course 1
Introduction to Predictive Modeling
4 lessons
20 practices
Initiate your understanding of predictive modeling by exploring the fundamental workings and purposes of these models. Gain insights into how predictive models can guide decision-making across industries and sectors.
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Course 2
Data Preprocessing for Predictive Modeling
5 lessons
Course 3
Regression Models for Prediction
4 lessons
Course 4
Advanced Machine Learning Models for Prediction
5 lessons
Course 5
Model Evaluation and Optimization
4 lessons
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24 practices
Unveil how preprocessing refines data to make predictive models more effective. Learn to handle missing values, outliers and categorical variables, ensuring data consistency and integrity.
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18 practices
Grasp the basics of using different regression models for predictive modeling. Learn how to establish polynomial, lasso and ridge regression models within Python.
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24 practices
As you become more proficient with regression models, this course will introduce you to more advanced models available in the Scikit-Learn library. Explore popular machine learning algorithms, including Support Vector Machines, decision trees, random forest and neural networks.
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18 practices
Any predictive regression model is only as good as its performance, this course delves into advanced techniques for evaluating and optimizing regression models. Explore sophisticated strategies to enhance predictive accuracy and model robustness.
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