Machine Learning Mastery: From Data to Advanced Classifiers

Machine Learning Mastery: From Data to Advanced Classifiers, available at $54.99, has an average rating of 5, with 32 lectures, based on 2 reviews, and has 8 subscribers.

You will learn about 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. This course is ideal for individuals who are Beginner and intermediate Python programmers who want to expand their skills into the field of machine learning. or Data analysts and data scientists who want to enhance their understanding and proficiency in machine learning techniques. or Professionals working with data who are interested in applying machine learning algorithms to solve real-world problems. or Students and researchers in computer science or related fields who want to gain practical knowledge and hands-on experience in machine learning. or 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. or Individuals seeking to evaluate and compare the performance of machine learning models using the Receiver Operator Characteristic (ROC) curve. It is particularly useful for Beginner and intermediate Python programmers who want to expand their skills into the field of machine learning. or Data analysts and data scientists who want to enhance their understanding and proficiency in machine learning techniques. or Professionals working with data who are interested in applying machine learning algorithms to solve real-world problems. or Students and researchers in computer science or related fields who want to gain practical knowledge and hands-on experience in machine learning. or 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. or Individuals seeking to evaluate and compare the performance of machine learning models using the Receiver Operator Characteristic (ROC) curve.

Enroll now: Machine Learning Mastery: From Data to Advanced Classifiers

Summary

Title: Machine Learning Mastery: From Data to Advanced Classifiers

Price: $54.99

Average Rating: 5

Number of Lectures: 32

Number of Published Lectures: 32

Number of Curriculum Items: 32

Number of Published Curriculum Objects: 32

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

• 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.

Who Should Attend

• Beginner and intermediate Python programmers who want to expand their skills into the field of machine learning.
• Data analysts and data scientists who want 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.

Target Audiences

• Beginner and intermediate Python programmers who want to expand their skills into the field of machine learning.
• Data analysts and data scientists who want 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.