Fundamentals of Machine Learning through Python

Fundamentals of Machine Learning through Python, available at Free, has an average rating of 4.39, with 26 lectures, based on 106 reviews, and has 2328 subscribers.

You will learn about Learn the art of data cleaning, handling missing values, and feature engineering to ensure high-quality datasets for effective machine learning model training Develop a solid understanding of Python essentials, control structures, and modular programming, providing a strong foundation for machine learning applications Dive into supervised learning techniques, mastering linear regression for numerical predictions, and logistic regression for effective classification Gain proficiency in assessing and optimizing model performance through cross-validation, addressing overfitting and underfitting, and fine-tuning Delve into ensemble methods such as Random Forest, Gradient Boosting, Support Vector Machine Apply acquired skills to a practical project, guiding learners through data preprocessing, model selection, training, and evaluation This course is ideal for individuals who are This course is designed for aspiring data enthusiasts, programmers, and beginners in machine learning who seek a comprehensive introduction to the field. Whether you're a Python novice or looking to transition into data science, this beginner-friendly journey will equip you with the essential skills to confidently explore and apply machine learning concepts in real-world scenarios. It is particularly useful for This course is designed for aspiring data enthusiasts, programmers, and beginners in machine learning who seek a comprehensive introduction to the field. Whether you're a Python novice or looking to transition into data science, this beginner-friendly journey will equip you with the essential skills to confidently explore and apply machine learning concepts in real-world scenarios.

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Summary

Title: Fundamentals of Machine Learning through Python

Price: Free

Average Rating: 4.39

Number of Lectures: 26

Number of Published Lectures: 26

Number of Curriculum Items: 27

Number of Published Curriculum Objects: 27

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

• Learn the art of data cleaning, handling missing values, and feature engineering to ensure high-quality datasets for effective machine learning model training
• Develop a solid understanding of Python essentials, control structures, and modular programming, providing a strong foundation for machine learning applications
• Dive into supervised learning techniques, mastering linear regression for numerical predictions, and logistic regression for effective classification
• Gain proficiency in assessing and optimizing model performance through cross-validation, addressing overfitting and underfitting, and fine-tuning
• Delve into ensemble methods such as Random Forest, Gradient Boosting, Support Vector Machine
• Apply acquired skills to a practical project, guiding learners through data preprocessing, model selection, training, and evaluation

Who Should Attend

• This course is designed for aspiring data enthusiasts, programmers, and beginners in machine learning who seek a comprehensive introduction to the field. Whether you're a Python novice or looking to transition into data science, this beginner-friendly journey will equip you with the essential skills to confidently explore and apply machine learning concepts in real-world scenarios.

Target Audiences

• This course is designed for aspiring data enthusiasts, programmers, and beginners in machine learning who seek a comprehensive introduction to the field. Whether you're a Python novice or looking to transition into data science, this beginner-friendly journey will equip you with the essential skills to confidently explore and apply machine learning concepts in real-world scenarios.