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Mastering Machine Learning: From Data Preparation to Advanced Classifiers
Machine Learning Mastery: From Data Import to Advanced Model Evaluation and Classifiers
Created By Abdurrahman TEKIN
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Course description
What You'll Learn
- Import and prepare data for analysis with efficiency and precision.
- Master data cleaning and preprocessing techniques to ensure data integrity.
- Discover effective data visualization methods to uncover insights.
- Utilize correlation heatmaps to analyze relationships between variables.
- Learn feature scaling and categorical variable handling for accurate modeling.
- Understand proper data splitting for training and testing.
- Implement advanced machine learning models, including SVC, RandomForestClassifier, XGBClassifier, KNeighborsClassifier, and LGBMClassifier.
- Evaluate models effectively using the Receiver Operator Characteristic (ROC) curve.
Requirements
- Basic understanding of programming concepts and Python programming language.
- Familiarity with data manipulation using libraries like Pandas and NumPy.
Description
Welcome to the ultimate Machine Learning course, where you will embark on a transformative journey into the world of data and advanced modeling techniques. Whether you're a beginner or an experienced practitioner, this course will equip you with the essential skills to excel in the field of machine learning.
In this comprehensive course, you will start by mastering the art of data handling. Learn how to import and clean data, ensuring that your datasets are pristine and ready for analysis. Discover powerful visualization techniques to gain deep insights and unravel hidden patterns within your data. Uncover the secrets of correlation analysis through captivating heatmap visualizations that reveal the intricate relationships between variables.
Next, dive into the realm of preprocessing, where you will explore various methods to prepare your data for modeling. Discover how to handle missing values, scale features, and encode categorical variables, laying the foundation for accurate and reliable predictions.
Data splitting is a critical step in the machine learning pipeline, and this course covers it extensively. Understand the importance of dividing your data into training and testing sets, ensuring optimal model performance and generalization.
The heart of this course lies in advanced modeling techniques. You will master a diverse range of classifiers, including the powerful Support Vector Classifier (SVC), the versatile RandomForestClassifier, the gradient-boosted XGBClassifier, the intuitive KNeighborsClassifier, and the lightning-fast LGBMClassifier. Gain a deep understanding of their inner workings, learn to fine-tune their hyperparameters, and witness their performance on real-world datasets.
To evaluate the effectiveness of your models, we delve into the Receiver Operator Characteristic (ROC) curve analysis. Discover how to interpret this essential evaluation metric and make informed decisions about model performance.
Throughout the course, you'll engage in hands-on projects, applying your knowledge to real-world datasets and honing your skills. Access to practical exercises and comprehensive resources will provide you with ample opportunities to reinforce your learning and solidify your understanding.
By the end of this course, you'll possess the expertise and confidence to tackle machine learning challenges head-on. Join us now and unlock the potential of machine learning to revolutionize your career and make a lasting impact in the world of data-driven insights.
Enroll today and embark on your journey to becoming a Machine Learning master!
Who This Course Is For
- Beginner and intermediate Python programmers who want to expand their skills into the field of machine learning.
- Data analysts and data scientists seeking to enhance their understanding and proficiency in machine learning techniques.
- Professionals working with data who are interested in applying machine learning algorithms to solve real-world problems.
- Students and researchers in computer science or related fields who want to gain practical knowledge and hands-on experience in machine learning.
- Anyone with a strong interest in machine learning and a desire to learn how to import, clean, visualize, preprocess, and model data using popular classifiers like SVC, RandomForestClassifier, XGBClassifier, KNeighborsClassifier, and LGBMClassifier.
- Individuals seeking to evaluate and compare the performance of machine learning models using the Receiver Operator Characteristic (ROC) curve.
Course content web
1. Introduction.mp4
2. Installing Jupyter.mp4
3. How to download Python files.mp4
1. Import Data.mp4
2. visualizing missing data in a dataset.mp4
3. calculating statistical information.mp4
4. checking for duplicate rows in the DataFrame.mp4
5. calculating the number of distinct values in each column.mp4
6. checking for missing or null values in the DataFrame.mp4
7. Cleaning the data.mp4
8. creating a new column called 'Label' in the DataFrame.mp4
9. creating a histogram plot.mp4
10. displaying the distribution of the data using a box plot.mp4
11. displaying the distribution of the data by the different categories.mp4
12. visualize the relationship between two variables with jointplot.mp4
13. calculating the correlation matrix of the DataFrame.mp4
14. creating a mask using NumPy.mp4
15. creating a color map using seaborn.mp4
16. creating a heatmap using seaborn.mp4
17. calculating the number of outliers.mp4
18. standardizing features.mp4
19. Hypothesis testing.mp4
20. Normalization.mp4
21. split the data into training and testing sets.mp4
22. Start traning SVC and Learn Hyperparameters.mp4
23. find the best hyperparameter.mp4
24. make predictions on the test data and avaluate the model.mp4
25. Train RandomForestClassifier.mp4
26. Train XGBClassifier.mp4
27. Train KNeighborsClassifier.mp4
28. Train LGBMClassifier.mp4
29. calculate the (ROC) curve and the (AUC) score.mp4
Files.zip
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