Title
Master Decision Trees and Random Forests with Scikit-learn
Get to the bottom of how to make predictions with them and enjoy your competitive edge. Jupyter Notebooks included.
What you will learn
Learn how decision trees and random forests make their predictions.
Learn how to use Scikit-learn for prediction with decision trees and random forests and for understanding the predictive structure of data sets.
Predict purchases and prices with decision trees and random forests.
Learn about each parameter of Scikit-learn’s methods DecisonTreeClassifier and RandomForestClassifier to define your decision tree or random forest.
Learn using the output of Scikit-learn’s DecisonTreeClassifier and RandomForestClassifier methods to investigate and understand your predictions.
Learn about how to work with imbalanced class values in the data and how noisy data can affect random forests’ prediction performance.
Growing decision trees: node splitting, node impurity, Gini diversity, entropy, mean squared and absolute error, Poisson deviance, feature thresholds.
Improving decision trees: cross-validation, grid/randomized search, tuning and minimal cost-complexity pruning, evaluating feature importance.
Creating random forests: bootstrapping, bagging, random feature selection, decorrelation of tree predictions.
Improving random forests: cross-validation, grid/randomized search, tuning, out-of-bag scoring, calibration of probability estimates.
Learn to use Scikit-learn’s methods DecisonTreeRegressor and RandomForestRegressor to fit and improve your regression decision tree or random forest.
Why take this course?
🚀 Master Decision Trees and Random Forests with Scikit-learn! 🌳🍃
Welcome to the comprehensive course, "Master Decision Trees and Random Forests with Scikit-learn," designed for data analysts and machine learning enthusiasts eager to dive deep into the world of predictive modeling. By the end of this course, you'll not only understand how to tackle classification and regression problems using decision trees and random forests but also enjoy a competitive edge in your data analysis projects. 🏆
Course Highlights:
- Interactive Learning: Engage with over 50 Jupyter Notebooks that serve as interactive labs for you to apply what you've learned.
- Real-World Applications: Learn through practical examples and real-world scenarios, ensuring the knowledge is both relevant and applicable to your projects.
- Expert Guidance: Instructor Wim Koevoets brings a wealth of experience, providing clear explanations and valuable insights throughout the course.
What You'll Learn:
- 🌱 Decision Trees for Classification and Regression: Understand how decision trees can be used for both classification and regression tasks, and when it's most effective to apply them.
- 🛠️ Elements of Growing Decision Trees: Discover the key components involved in the creation of decision trees, from selecting features to splitting strategies.
- ⚙️ Scikit-learn Parameters: Master the parameters available for customizing decision tree classifiers and regressors, as well as random forest models, to suit your data's unique characteristics.
- 📊 Making Predictions with Scikit-learn: Learn how to fit, prune/tune, and investigate your decision tree and random forest models for optimal performance.
- 🌲 Random Forests Explained: Uncover the ensemble method behind random forests, and understand why it's a powerful predictive tool in the machine learning arsenal.
- ✅ Characteristics of Fitted Models: Gain insights into what to look for when you've fitted a model, including how to interpret the results and make data-driven decisions.
- 🔍 Understanding Prediction Performance: Learn how to evaluate your models and understand the metrics that matter most for your predictions.
- 🛠️ Practical Project Guidance: Carry out a complete prediction project from start to finish, utilizing decision trees and random forests at each step.
Course Structure: Each lesson is distilled into an easy-to-digest video, followed by hands-on practice in Jupyter notebooks. These materials are designed to reinforce the concepts taught in the videos and provide a solid foundation for your predictive modeling skills.
Student Testimonials:
- 🚀 "The valuable information provided in this course has been instrumental in my understanding of decision trees and random forests."
- 🎓 "Clear explanations and helpful practice activities made the complex topics easy to follow."
- 🧙♂️ "Wim Koevoets' knowledgeable instruction demystified the world of machine learning for me."
- ✨ "The Jupyter notebooks have been an invaluable resource, allowing me to practice and experiment with real-world data."
Join us on this journey to harness the power of decision trees and random forests! With Scikit-learn at your fingertips, you're set for success. 🌟
Enroll now and take the first step towards mastering predictive modeling with confidence. Let's unlock the predictive potential of your data together! 🔓✨
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| Submit by | Date | Coupon Code | Discount | Emitted/Used | Status |
|---|---|---|---|---|---|
| - | 26/08/2022 | DTRFAUG_22FRCS | 100% OFF | 1000/948 | expired |
| - | 26/06/2023 | A828316DCB4B1FB8C97B | 100% OFF | 100/93 | expired |
| - | 24/07/2023 | DCE3CAC3FF7E61F7B5B8 | 100% OFF | 100/48 | expired |