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 youre 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 youre 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 youre 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 youre 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.

Unlock the potential of machine learning with our comprehensive course, “Mastering Machine Learning: From Fundamentals to Practical Projects with Python and Scikit-Learn.” Tailored for aspiring data enthusiasts and programmers, this course is an immersive journey through the key pillars of machine learning, ensuring a strong foundation and practical proficiency.

Begin with Python fundamentals, covering variables, control structures, and modular programming, before delving into the heart of data science: data preparation. Learn to wield Python for data cleaning, handle missing values, and engineer features to optimize dataset quality. Transition seamlessly into supervised learning, mastering linear and logistic regression for numerical predictions and categorical classifications.

Navigate the intricate landscape of model evaluation and validation, ensuring your models generalize well to unseen data. Harness the power of Scikit-Learn, building and training models with its intuitive interface. Explore advanced topics, from ensemble methods like Random Forest and Gradient Boosting to the complexity-solving capabilities of Support Vector Machines.

The course crescendos with a hands-on project, where learners apply acquired skills to real-world scenarios, from data preprocessing to model selection and evaluation. Emerging from this course, you’ll possess the confidence to navigate the machine learning landscape, equipped with practical skills, project experience, and a deepened understanding of Python and Scikit-Learn. Start your machine learning journey today!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to Course

Lecture 2: Setting Up Google Colaboratory

Lecture 3: Importance of Machine Learning

Chapter 2: Python Fundamentals for Machine Learning

Lecture 1: Introduction to Python

Lecture 2: Variables and Operators

Lecture 3: Control Structures

Lecture 4: Functions

Lecture 5: Modules

Lecture 6: Intro to Data Structures

Chapter 3: Data Preparation: The Foundation of ML Success

Lecture 1: Introduction to Data Processing

Lecture 2: Transforming Data

Lecture 3: Data Visualization

Chapter 4: Supervised Learning

Lecture 1: Introduction to supervised learning

Lecture 2: Linear Regression

Lecture 3: Logistic Regression

Chapter 5: Model Evaluation and Optimization

Lecture 1: Metrics

Lecture 2: Cross Validation

Lecture 3: Overfitting or Underfitting Models

Lecture 4: Hyperparameter Tuning

Chapter 6: Scikit-Learn

Lecture 1: Introduction to scikit-learn

Lecture 2: Overview of documentation

Chapter 7: Advanced Machine Learning Models

Lecture 1: RandomForest and GradientBoosting

Lecture 2: KNN

Lecture 3: SVM

Chapter 8: Project

Lecture 1: Project Introduction

Chapter 9: Conclusion

Lecture 1: Concluding Remarks

Instructors

Meenakshi Nair
Instructor At Udemy

Rating Distribution

1 stars: 0 votes

2 stars: 2 votes

3 stars: 11 votes

4 stars: 37 votes

5 stars: 56 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

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