Ultimate ML Bootcamp #3: Logistic Regression
Ultimate ML Bootcamp #3: Logistic Regression, available at Free, with 15 lectures, and has 23 subscribers.
You will learn about Understand the fundamentals and applications of logistic regression in machine learning. Apply logistic regression to real-world data for binary classification problems. Evaluate model performance using metrics like ROC curves and confusion matrices. Implement cross-validation techniques to ensure the robustness of logistic regression models. This course is ideal for individuals who are This course is ideal for aspiring data scientists and analysts seeking to deepen their understanding of machine learning techniques, specifically in classification and predictive modeling. It is particularly useful for This course is ideal for aspiring data scientists and analysts seeking to deepen their understanding of machine learning techniques, specifically in classification and predictive modeling.
Enroll now: Ultimate ML Bootcamp #3: Logistic Regression
Summary
Title: Ultimate ML Bootcamp #3: Logistic Regression
Price: Free
Number of Lectures: 15
Number of Published Lectures: 15
Number of Curriculum Items: 15
Number of Published Curriculum Objects: 15
Original Price: Free
Quality Status: approved
Status: Live
What You Will Learn
- Understand the fundamentals and applications of logistic regression in machine learning.
- Apply logistic regression to real-world data for binary classification problems.
- Evaluate model performance using metrics like ROC curves and confusion matrices.
- Implement cross-validation techniques to ensure the robustness of logistic regression models.
Who Should Attend
- This course is ideal for aspiring data scientists and analysts seeking to deepen their understanding of machine learning techniques, specifically in classification and predictive modeling.
Target Audiences
- This course is ideal for aspiring data scientists and analysts seeking to deepen their understanding of machine learning techniques, specifically in classification and predictive modeling.
Welcome to the third chapter of Miuul’s Ultimate ML Bootcamp—a comprehensive series crafted to elevate your expertise in the realm of machine learning and artificial intelligence. This chapter, Ultimate ML Bootcamp #3: Logistic Regression, expands on the knowledge you’ve accumulated thus far and dives into a pivotal technique used extensively across classification tasks—logistic regression.
In this chapter, we explore the nuances of logistic regression, a fundamental method for classification in predictive modeling. We’ll begin by defining logistic regression and discussing its critical role in machine learning, particularly in scenarios where outcomes are categorical. You’ll learn about the logistic function and how it is used to model probabilities that vary between 0 and 1, thus facilitating binary classification tasks.
The journey continues as we delve into gradient descent—a powerful optimization algorithm—to refine our logistic regression models. You’ll grasp how to implement gradient descent to minimize the loss function, a key step in improving the accuracy of your model.
Further, we’ll cover essential model evaluation metrics specific to classification, such as accuracy, precision, recall, and the F1-score. Tools like the confusion matrix will be explained, providing a clear picture of model performance, alongside discussions on setting the optimal classification threshold.
Advancing through the chapter, you’ll encounter the ROC curve and understand its significance in evaluating the trade-offs between true positive rates and false positive rates. The concept of LOG loss will also be introduced as a measure of model accuracy, providing a quantitative basis to assess model performance.
Practical application is a core component of this chapter. We will apply logistic regression to a real-life scenario—predicting diabetes onset. This section includes a thorough walk-through from exploratory data analysis (EDA) and data preprocessing, to building the logistic regression model and evaluating its performance using various metrics.
We conclude with in-depth discussions on model validation techniques, including k-fold cross-validation, to ensure your model’s robustness and reliability across unseen data.
This chapter is structured to provide a hands-on learning experience with practical exercises and real-life examples to solidify your understanding. By the end of this chapter, you’ll not only be proficient in logistic regression but also prepared to tackle more sophisticated machine learning challenges in the upcoming chapters of Miuul’s Ultimate ML Bootcamp. We are thrilled to guide you through this vital segment of your learning journey. Let’s begin exploring the intriguing world of logistic regression!
Course Curriculum
Chapter 1: Logistic Regression
Lecture 1: Course Materials
Lecture 2: What is Logistic Regression?
Lecture 3: Gradient Descent for Logistic Regression
Lecture 4: Model Evaluation in Classification Problems
Lecture 5: Confusion Matrix
Lecture 6: Classification Threshold
Lecture 7: ROC Curve
Lecture 8: LOG Loss
Lecture 9: Application: Diabetes Prediction with Logistic Regression
Lecture 10: EDA
Lecture 11: Data Preprocessing
Lecture 12: Logistic Regression Model
Lecture 13: Model Evaluation
Lecture 14: Model Validation
Lecture 15: Cross Validation
Instructors
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Miuul Data Science & Deep Learning
Data Science Team of Miuul.com
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