Having watched quite a lot regression videos I can say confidently this is something which sums up and condenses each and every thing for a beginner to grasp linear regression smoothly(see what I did there?). Thank you so much for making this public!
This is a very good lecture. The discussion of the Loss Function is the first time I’ve see someone explain it so clearly and gives the intuition of what argmin really means
This lecture was so well explained! The baby-steps approach is so clever. By understanding the simplest cases one can grow from there! Thank you Dmitry!
🎯 Key Takeaways for quick navigation: 00:11 📚 Introduction to the Course - This section introduces the "Introduction to Machine Learning" course. - The course aims to provide a basic understanding of machine learning concepts. - It's designed to prepare students for more advanced machine learning courses. 03:32 🧠 What is Machine Learning? - Explains the definition of machine learning as the study of algorithms that improve through experience. - Contrasts traditional problem-solving approaches with machine learning. - Discusses the difference in emphasis between statistics and machine learning. 11:56 🕵️ Types of Machine Learning Problems - Introduces the three main types of machine learning problems: supervised, unsupervised, and reinforcement learning. - Focuses on supervised learning and briefly mentions unsupervised learning. - Explains that reinforcement learning is not covered in this course. 14:59 🔍 Linear Regression as a Starting Point - Discusses why the course begins with linear regression, a simple and classical method. - Introduces the concept of a loss function for linear regression. - Mentions the idea of "baby linear regression" where the intercept is constrained to zero. 21:24 📈 Optimization and Finding the Minimum - Discusses the concept of finding the minimum of the loss function to estimate the beta values in linear regression. - Explains that the loss function results in a quadratic polynomial. - Highlights the need to find the estimate (beta hat) given the training data. 23:39 🧐 Gradient Descent in Linear Regression - Gradient Descent is a method to find the minimum of a function. - The update rule for Gradient Descent involves a learning rate. - The choice of learning rate impacts the convergence of Gradient Descent. 32:50 🧐 Extending to Simple Linear Regression - Simple Linear Regression involves two parameters: the slope (beta1) and the intercept (beta0). - The loss function for Simple Linear Regression forms a 3D surface. - Gradient Descent can still be used with partial derivatives to optimize in multiple dimensions. Made with HARPA AI
you honestly do not need a prerequisite to understanding what he is saying. You just need to listen and follow. google the terms you do not understand and just take notes the understanding actually come after a certain period of time
Title: "Foundations of Machine Learning: Walking Through Linear Regression" Introduction to basic concepts of machine learning - Course aims to prepare students for advanced machine learning courses - Focus on developing key concepts and intuitions behind machine learning Machine learning aims to detect patterns in data and make useful predictions in challenging situations. - Machine learning involves training an algorithm by giving it data and answers, allowing it to discriminate without explicit rules. - The focus of machine learning is on making useful predictions rather than learning about the world. Introduction to different types of machine learning problems - Supervised learning involves labeled data to distinguish classes - Unsupervised learning clusters data without labels, focusing on different kinds of animals Simple linear regression involves predicting a continuous variable based on one predictor. - - It uses a linear function with two parameters - intercept (beta zero) and slope (beta one) to fit the data. - - The loss function for linear regression is the mean squared error, which measures the squared deviation between actual and predicted values and is used to optimize the model. Introduction to Baby Linear Regression with a Single Parameter Beta - The model simplifies linear regression by ignoring the intercept and using only one parameter, beta. - The optimization process involves finding the minimum of a quadratic loss function using baby gradient descent with a learning rate. Understanding the challenges with non-convex functions and choosing the right learning rate in gradient descent. - Non-convex functions can lead to challenges in finding the global minimum using gradient descent. - Choosing the right learning rate is crucial as a large learning rate can cause divergence, while a small learning rate can lead to slow convergence. Explaining gradient descent for simple linear regression - Computing gradient using derivative with respect to beta not x - Utilizing derivative to update beta and converge to minimum point Understanding beta as a vector in two dimensions and its update rule using gradient - Beta can be considered as a vector with two coordinates, beta 0 and beta 1 - The gradient is a vector consisting of partial derivatives along each coordinate
i want to ask something about the course...there are many courses related to ML on this channel but where to start....what is the 1st course that i pick???anybody please tell me
@@tilakkalyan there are some courses on this channel like: Probalistic ML Statistical ML Math for ML Intro to ML and many more related to ML but i want to ask that from these all course what should be the 1st one to learn...
Having watched quite a lot regression videos I can say confidently this is something which sums up and condenses each and every thing for a beginner to grasp linear regression smoothly(see what I did there?). Thank you so much for making this public!
Absolutely fantastic explanation. Recommendation of freely available literature is golden, too!
This is a very good lecture. The discussion of the Loss Function is the first time I’ve see someone explain it so clearly and gives the intuition of what argmin really means
This lecture was so well explained!
The baby-steps approach is so clever.
By understanding the simplest cases one can grow from there!
Thank you Dmitry!
You are the best teacher in the world thanks
Thanks so much! The simple introduction makes all the generalized equations a lot easier to understand!
This whole channel is amazing. Thank you so much
🎯 Key Takeaways for quick navigation:
00:11 📚 Introduction to the Course
- This section introduces the "Introduction to Machine Learning" course.
- The course aims to provide a basic understanding of machine learning concepts.
- It's designed to prepare students for more advanced machine learning courses.
03:32 🧠 What is Machine Learning?
- Explains the definition of machine learning as the study of algorithms that improve through experience.
- Contrasts traditional problem-solving approaches with machine learning.
- Discusses the difference in emphasis between statistics and machine learning.
11:56 🕵️ Types of Machine Learning Problems
- Introduces the three main types of machine learning problems: supervised, unsupervised, and reinforcement learning.
- Focuses on supervised learning and briefly mentions unsupervised learning.
- Explains that reinforcement learning is not covered in this course.
14:59 🔍 Linear Regression as a Starting Point
- Discusses why the course begins with linear regression, a simple and classical method.
- Introduces the concept of a loss function for linear regression.
- Mentions the idea of "baby linear regression" where the intercept is constrained to zero.
21:24 📈 Optimization and Finding the Minimum
- Discusses the concept of finding the minimum of the loss function to estimate the beta values in linear regression.
- Explains that the loss function results in a quadratic polynomial.
- Highlights the need to find the estimate (beta hat) given the training data.
23:39 🧐 Gradient Descent in Linear Regression
- Gradient Descent is a method to find the minimum of a function.
- The update rule for Gradient Descent involves a learning rate.
- The choice of learning rate impacts the convergence of Gradient Descent.
32:50 🧐 Extending to Simple Linear Regression
- Simple Linear Regression involves two parameters: the slope (beta1) and the intercept (beta0).
- The loss function for Simple Linear Regression forms a 3D surface.
- Gradient Descent can still be used with partial derivatives to optimize in multiple dimensions.
Made with HARPA AI
you honestly do not need a prerequisite to understanding what he is saying. You just need to listen and follow. google the terms you do not understand and just take notes the understanding actually come after a certain period of time
Title: "Foundations of Machine Learning: Walking Through Linear Regression"
Introduction to basic concepts of machine learning
- Course aims to prepare students for advanced machine learning courses
- Focus on developing key concepts and intuitions behind machine learning
Machine learning aims to detect patterns in data and make useful predictions in challenging situations.
- Machine learning involves training an algorithm by giving it data and answers, allowing it to discriminate without explicit rules.
- The focus of machine learning is on making useful predictions rather than learning about the world.
Introduction to different types of machine learning problems
- Supervised learning involves labeled data to distinguish classes
- Unsupervised learning clusters data without labels, focusing on different kinds of animals
Simple linear regression involves predicting a continuous variable based on one predictor.
- - It uses a linear function with two parameters - intercept (beta zero) and slope (beta one) to fit the data.
- - The loss function for linear regression is the mean squared error, which measures the squared deviation between actual and predicted values and is used to optimize the model.
Introduction to Baby Linear Regression with a Single Parameter Beta
- The model simplifies linear regression by ignoring the intercept and using only one parameter, beta.
- The optimization process involves finding the minimum of a quadratic loss function using baby gradient descent with a learning rate.
Understanding the challenges with non-convex functions and choosing the right learning rate in gradient descent.
- Non-convex functions can lead to challenges in finding the global minimum using gradient descent.
- Choosing the right learning rate is crucial as a large learning rate can cause divergence, while a small learning rate can lead to slow convergence.
Explaining gradient descent for simple linear regression
- Computing gradient using derivative with respect to beta not x
- Utilizing derivative to update beta and converge to minimum point
Understanding beta as a vector in two dimensions and its update rule using gradient
- Beta can be considered as a vector with two coordinates, beta 0 and beta 1
- The gradient is a vector consisting of partial derivatives along each coordinate
Wonderful explanations! Make a hard subject appears simple.
Thank you so much! Way better than my Professor at Uni Ulm who just spams you with formulas
Thanks for this channel
Отличное объяснение! Спасибо ❤
Do you have the link to the course?
Great lecture :)
Good 👍
Hello , are the slides for the videos lectures available . I know the slides for other courses in the series are available but not this one ?
thank you
beta_1 = ((1-n).Sum(y_i.x_i))/(n.Sum(x_i^2) - Sum(x_i)^2), beta_0 =(-Sum(y_i) - Sum(x_i).beta_1)/n. Did someone solve the exercise in the end.
machine learning vs pattern recognition?
is there any website of this course? can we access the notebooks?
great lecture!! 29:00 I think you increase beta to decrease the loss since the derivative is negative
It's true but this equation is true for increases beta or decrease it's depends on the signal slope of the curve of MSE
i want to ask something about the course...there are many courses related to ML on this channel but where to start....what is the 1st course that i pick???anybody please tell me
1) Basics of ML
2) Basics of Maths (Statistics)
3) Python Basics
@@tilakkalyan there are some courses on this channel like:
Probalistic ML
Statistical ML
Math for ML
Intro to ML
and many more related to ML but i want to ask that from these all course what should be the 1st one to learn...
Нашел интересный жорнал про всякие статистические данные, довольно занятно)
Terrible microphone.
The mistake was putting the mic to his nose instead of mouth...
Focus on the content.
@@JamesSmith-bo3po Sure - once I am able to hear it.
@@antonvesty2256😂