Machine Learning Mastery: From Data to Advanced Classifiers
Mastering Machine Learning: From Data Import to Model Evaluation with Advanced Classifiers
Updated on Jun, 2025
Language - English
Development ,Data Science,Machine Learning
Lectures -31
Resources -1
Duration -2.5 hours
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Course 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 how 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 will work on 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 will 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!
Goals
- Importing and preparing data for analysis.
- Cleaning and preprocessing techniques for data integrity.
- Effective data visualization methods.
- Understanding and utilizing correlation heatmaps.
- Preprocessing steps for feature scaling and handling categorical variables.
- Proper data splitting for training and testing.
- Implementation of machine learning models: Support Vector Classifier (SVC), RandomForestClassifier, XGBClassifier, KNeighborsClassifier, LGBMClassifier
- Evaluation using Receiver Operator Characteristic (ROC) curve.
Prerequisites
- Basic understanding of programming concepts and Python programming language.
- Familiarity with data manipulation using libraries such as Pandas and NumPy.
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
2 Lectures
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Introduction
02:01
02:01
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Installing Jupyter
02:16
02:16
Course Contents
29 Lectures
Instructor Details
Abdurrahman Tekin
Abdurrahman Tekin is a passionate academic and educator driven by a deep fascination with cutting-edge technologies and a commitment to sharing knowledge. Currently pursuing his Ph.D. at the prestigious Nanjing University of Aeronautics and Astronautics, Abdurrahman's research delves into the captivating realm of "Multi-Objective Airfoil/Wing Shape Optimization using Deep Learning, Bayesian methods, and Knowledge-Based Modeling."
With a profound understanding of artificial intelligence, programming, and language learning, Abdurrahman has embarked on a mission to empower learners worldwide through his online teaching endeavors. As an esteemed instructor on Tutorialspoint, he has successfully guided over 50,000 students from 166 different countries, imparting invaluable skills in AI, Python, English, and Chinese.
Beyond the virtual classroom, Abdurrahman's enthusiasm for education extends to his YouTube channel, where he shares his experiences and insights with a growing community of over 8,000 followers. Through engaging videos, he provides a unique glimpse into his academic journey and offers practical advice to aspiring learners.
Abdurrahman's multifaceted approach to education reflects his unwavering commitment to lifelong learning and his belief in the transformative power of knowledge. With a unique blend of academic rigor and a passion for teaching, he continues to inspire and empower individuals across the globe, paving the way for a future where innovation and education go hand in hand.
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