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.

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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Installing Jupyter

Lecture 3: How to download Python files

Chapter 2: Course Contents

Lecture 1: Import Data

Lecture 2: 2 visualizing missing data in a dataset

Lecture 3: 3 calculating statistical information

Lecture 4: 4 checking for duplicate rows in the DataFrame

Lecture 5: 5 calculating the number of distinct values in each column

Lecture 6: 6 checking for missing or null values in the DataFrame

Lecture 7: 7 Cleaning the data

Lecture 8: 8 creating a new column called Label in the DataFrame

Lecture 9: 9 creating a histogram plot

Lecture 10: 10 displaying the distribution of the data using a box plot

Lecture 11: 11 displaying the distribution of the data by the different categories

Lecture 12: 12 visualize the relationship between two variables with jointplot

Lecture 13: 13 calculating the correlation matrix of the DataFrame

Lecture 14: 14 creating a mask using NumPy

Lecture 15: 15 creating a color map using seaborn

Lecture 16: 16 creating a heatmap using seaborn

Lecture 17: 17 calculating the number of outliers

Lecture 18: 18 standardizing features

Lecture 19: 19 Hypothesis testing

Lecture 20: 20 Normalization

Lecture 21: 21 split the data into training and testing sets

Lecture 22: 22 Start traning SVC and Learn Hyperparameters

Lecture 23: 23 find the best hyperparameter

Lecture 24: 24 make predictions on the test data and avaluate the model

Lecture 25: 25 Train RandomForestClassifier

Lecture 26: 26 Train XGBClassifier

Lecture 27: 27 Train KNeighborsClassifier

Lecture 28: 28 Train LGBMClassifier

Lecture 29: 29 calculate the (ROC) curve and the (AUC) score

Instructors

Abdurrahman TEKIN
PhD student

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Frequently Asked Questions

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