Udemy – Machine Learning Mastery: From Data to Advanced Classifiers 2024-2

Udemy – Machine Learning Mastery: From Data to Advanced Classifiers 2024-2 Downloadly IRSpace

Apr 18, 2025 - 19:41 Updated: Aug 17, 2025 - 19:06

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Udemy – Machine Learning Mastery: From Data to Advanced Classifiers 2024-2

Machine Learning Mastery: From Data to Advanced Classifiers. This comprehensive course will introduce you to the complete process of working with data and machine learning modeling. First, you will learn the basic concepts of data management, including importing, cleaning, and preparing data for analysis. Advanced data visualization techniques will help you extract valuable insights from data and identify complex relationships between variables through heat maps. The next stage of the course is dedicated to data preprocessing, where you will learn various methods such as handling missing values, scaling features, and encoding categorical variables. An important part of the course is to divide the data into training and test sets to ensure optimal model performance. In the modeling section, you will be introduced to a wide range of classifiers, including SVC, RandomForest, XGBoost, KNN, and LightGBM. Tuning the parameters of these models and evaluating their performance through ROC curves are other important topics in this section. Throughout the course, you will strengthen your skills by working on practical projects and using real data sets. Diverse exercises and rich educational resources will help you consolidate the acquired knowledge well.

What you will learn:

Import and prepare data for analysis.
Cleaning and preprocessing techniques for data integrity.
Effective data visualization methods.
Understanding and using correlation heatmaps.
Preprocessing steps for feature scaling and categorical variable management.
Properly split data for training and testing.
Implementing machine learning models: Support Vector Classifier (SVC), RandomForestClassifier, XGBClassifier, KNeighborsClassifier, LGBMClassifier.
Evaluation using Receiver Operator Characteristic (ROC) curve.

Who is this course suitable for?

Beginner and intermediate Python programmers who want to expand their skills in machine learning.
Data analysts and data scientists who want to increase their understanding and skills in machine learning techniques.
Data scientists who are interested in using 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 who is passionate about machine learning and wants to learn how to ingest, clean, visualize, preprocess, and model data using popular classifiers such as SVC, RandomForestClassifier, XGBClassifier, KNeighborsClassifier, and LGBMClassifier.
People looking to evaluate and compare the performance of machine learning models using the Receiver Operator Characteristic (ROC) curve.