Using ChatGPT as Your Personalized Teacher

Machine Learning and Data Science are the two most essential technologies in Industry 4.0. Data Science refers to extracting meaningful insights from data, while Machine Learning enables the computer to learn independently without being explicitly programmed. Mastering these fields requires a solid understanding of fundamental concepts, hands-on experience, and guidance from mentors. Traditional learning methods, like attending lectures, reading books, making notes, etc., can be inflexible, expensive, and time-consuming. This is where ChatGPT can be your personalized tutor.

In this post, you will uncover the benefits of ChatGPT as a personal tutor for machine learning and data science enthusiasts. In particular, you will learn

• How to let ChatGPT guide you to learn abstract algebra
• How to let ChatGPT help you prepare for a data science interview

Get started and apply ChatGPT with my book Maximizing Productivity with ChatGPT. It provides real-world use cases and prompt examples designed to get you using ChatGPT quickly.

Using ChatGPT as Your Personalized Teacher
Picture generated by Adrian Tam using Stable Diffusion. Some rights reserved.

Overview

This post is divided into three parts; they are:

• Mastering Linear Algebra in 12 Weeks
• Self-Quizzing for a Machine Learning Interview
• Prompting Tips to Enhance Learning Experience

Mastering Linear Algebra in 12 Weeks

Let’s say you heard about linear algebra, interested in it but have no idea what it is. You know that linear algebra is an important skill for becoming a data scientist. You want to learn linear algebra seriously.

This is a good chance to let ChatGPT guide you by generating a lesson plan. To generate relevant and accurate responses, your prompt should be specific and clearly state your end objectives. You should cover in your prompt:

• What do you want to learn?
• Is your goal time-bound?
• Is there something that you want to focus more on?

Keeping in view the above scenario, here is the basic template that you can use:

I am interested in mastering {Subject} with a focus on {Topic}. I would like to create a {Number of Weeks/Months} study plan that will help me achieve this goal.

Can you please help me by:
1. {Subgoal-1}
2. {Subgoal-2} and so on…

You can also specify your current level of expertise. The following is an example of what you prompted to ChatGPT:

I am interested in mastering abstract linear algebra with a focus on topics relevant to data science and machine learning. I would like to create a 12-week study plan that will help me achieve this goal.

Can you please help me by:
1. Identifying the key concepts that I need to learn in abstract linear algebra weekly
2. Recommending the best resources for learning these concepts.

The response from ChatGPT was as follows:

Certainly! Here’s a breakdown of key concepts to learn in abstract linear algebra weekly, along with recommended resources:

Week 1: Introduction to Linear Algebra and Vectors

Learn about the basic concepts of linear algebra, such as vectors, vector spaces, and linear combinations.
Familiarize yourself with vector operations, including addition, subtraction, and scalar multiplication.
Recommended resources:
“Introduction to Linear Algebra” by Gilbert Strang
“Linear Algebra and Its Applications” by David C. Lay

Week 2: Matrices and Matrix Operations

Study matrices, their representation, and operations like addition, subtraction, and scalar multiplication.
Explore matrix multiplication and properties such as associativity and distributivity.
Recommended resources:
“Linear Algebra and Its Applications” by David C. Lay
“Linear Algebra Done Right” by Sheldon Axler

Week 3: Systems of Linear Equations

Understand systems of linear equations and their solutions.
Learn about Gaussian elimination, row echelon form, and reduced row echelon form.
Explore concepts of rank, null space, and column space.
Recommended resources:
“Introduction to Linear Algebra” by Gilbert Strang
“Linear Algebra and Its Applications” by David C. Lay

…

As ChatGPT has a maximum token limit for each response so if you experience an incomplete response, you can use this prompt:

Can you please continue and complete your response?

Now you get a complete roadmap with topics relevant to your end goal and all the resources you need per topic. Is that enough? You may also want ChatGPT to summarize the topic and provide some practice questions to solidify your understanding. In this case, you can use a follow-up prompt to create an interactive learning experience.

Thank you for sharing the 12-week schedule. I am excited to get started!
Can you please explain the first concept of Week 1
with examples? I would also appreciate it if you could provide the practice problems to solidify my understanding of the concept.

The response from ChatGPT was:

Certainly! In Week 1, the first concept is “Introduction to Linear Algebra and Vectors.” Let’s dive into it:

Concept Explanation:
Linear algebra is a branch of mathematics that deals with vector spaces and linear transformations.
Vectors are mathematical objects that have both magnitude and direction, and they are fundamental in linear algebra.
Vectors can be represented as column vectors or row vectors and can have various dimensions.

Example:
Let’s consider a two-dimensional vector represented as v = [2, 5]. Here, the vector has two components: 2 and 5. The first component (2) represents the magnitude or value in the x-direction, and the second component (5) represents the magnitude or value in the y-direction.

Practice Problems:
Given vectors u = [1, -3] and v = [4, 2], calculate the sum u + v.
Determine the magnitude (length) of the vector u = [3, -4].
Find the unit vector in the same direction as v = [2, 7].
Given the vectors u = [1, 2, 3] and v = [4, -1, 0], calculate the dot product u · v.
Verify whether the vectors u = [1, 2, -3] and v = [-2, 4, -6] are parallel (collinear) by checking if one is a scalar multiple of the other.
Solving these practice problems will help solidify your understanding of vectors and their properties. Good luck!

With the help of ChatGPT, you are now able to master the concepts of abstract linear algebra. You can learn effectively and have a virtual tutor available 24/7 to clear your doubts and suggest additional resources for better understanding.

Self-Quizzing for a Machine Learning Interview

Let’s consider another example.

Assume you are interviewing for the job of Junior Machine Learning Engineer. To perform better in the interview, you should train yourself to think fluently for the interview questions. Can you make ChatGPT help you practice for the interview?

A good prompt is important. It is better to divide the problem statement into sub-goals, which you can use this basic template:

I want you to act as my {Subject/Position} Interviewer. Conduct my {Interview Type} interview for the {Position/Role}. Please ask me a series of questions to evaluate my knowledge and skills in {Areas}, and provide feedback on my responses unless I ask you to stop.

So this is the prompt you feed into ChatGPT:

I want you to act as my Machine Learning Interviewer. Conduct my technical interview for the Junior Machine Learning Engineer position. Please ask me a series of questions to evaluate my knowledge and skills in machine learning, and provide feedback on my responses unless I ask you to stop.

Which you may see this response from ChatGPT:

Sure! I’d be happy to conduct your technical interview for the Junior Machine Learning Engineer position. I’ll ask you a series of questions related to machine learning, and I’ll provide feedback on your responses. Let’s begin!

Question 1: What is the difference between supervised learning and unsupervised learning? Provide examples of each.

(Note: Feel free to take your time and provide a detailed response. I’ll wait for your answer before proceeding to the next question.)

Scenario 01: Providing Correct and Detailed Response

If you have a solid understanding of the asked concept, you may provide a detailed and correct response, such as the following:

“In Supervised learning, we have a labeled dataset while in Unsupervised Learning we have an unlabeled dataset. For example, sentiment analysis can be an example of supervised learning where the text has been labeled with correct output for training purposes. Clustering the unlabeled dataset into groups based on similarities is an example of an unsupervised learning problem”

Then ChatGPT will appraise you:

Great job on explaining the difference between supervised and unsupervised learning! You correctly mentioned that supervised learning involves using labeled data, where the algorithm learns from input-output pairs to make predictions or classifications. Sentiment analysis is indeed a good example of supervised learning, where sentiment labels (positive, negative, neutral) are provided for training.

You also mentioned unsupervised learning correctly. In unsupervised learning, the algorithm works with unlabeled data to find patterns, structures, or groupings in the data. Clustering is a commonly used technique in unsupervised learning, where similar data points are grouped based on their features or attributes.

Overall, your response demonstrates a good understanding of the difference between supervised and unsupervised learning, and you provided relevant examples. Well done!

Let’s move on to the next question.

Question 2: What is the purpose of regularization in machine learning algorithms? How does it help in preventing overfitting?

Scenario 02: Concise but Correct Response

However, if you know what is “regularization” and but not aware of the ins and outs, you may be able to provide only a short answer:

“Regularization adds a penalty to the loss function hence preventing overfitting”

In this case, ChatGPT will tell you to enrich your answer:

That’s a good start! Regularization is indeed used to prevent overfitting in machine learning algorithms. Let me provide you with a more detailed explanation.

Regularization is a technique that introduces a penalty term to the loss function during training. The penalty term is based on the complexity or magnitude of the model’s parameters. The purpose of this penalty term is to discourage the model from fitting the training data too closely, which can lead to overfitting.

Overfitting occurs when a model becomes too specialized in capturing the noise or idiosyncrasies of the training data, resulting in poor generalization to unseen data. Regularization helps to address this issue by adding a regularization term to the loss function, which controls the complexity of the model. The regularization term encourages the model to find a balance between fitting the training data well and keeping the model’s parameters small or constrained.

By including regularization, the model is penalized for having large or complex parameter values, which helps prevent overfitting. It encourages the model to generalize better to unseen data by finding a simpler and more robust representation of the underlying patterns in the data.

In summary, regularization adds a penalty to the loss function to prevent overfitting by discouraging complex or large parameter values in the model. This promotes better generalization to new, unseen data.

Well done on your response! If you have any questions or would like to discuss further, feel free to let me know before moving on to the next question.

You can observe how ChatGPT provided a detailed explanation of the concept. It also allows you to clear any of your concerns before it can move over to the next question just like a real tutor.

Isn’t it cool? Now, let us move over to our last scenario by first prompting ChatGPT to continue with the next question. Then it may respond with:

Of course! Let’s proceed to the next question.

Question 3: Explain the bias-variance tradeoff in machine learning and its relationship to model complexity.

(Take your time to provide a detailed response, and feel free to ask for clarification if needed.)

Scenario 03: Incorrect Response

Let’s say you did not know what is bias-variance tradeoff and gave an incorrect response:

“The bias-variance tradeoff is a trade-off between the model’s accuracy and its complexity. As the complexity of a model increases, bias increase and variance decreases”

In this case, you will see ChatGPT trying to correct you:

I appreciate your attempt at explaining the bias-variance tradeoff. However, the relationship between bias and variance is slightly different than what you mentioned. Allow me to provide a more accurate explanation:

The bias-variance tradeoff is a fundamental concept in machine learning that deals with the relationship between the bias and variance of a model. Bias refers to the error introduced by approximating a real-world problem with a simplified model, making assumptions that may not hold. Variance, on the other hand, refers to the model’s sensitivity to fluctuations in the training data, meaning it captures noise or random variations rather than the true underlying patterns.

The tradeoff arises when adjusting the complexity of a model. A model with high bias tends to be overly simplistic, making strong assumptions that may not match the underlying data distribution. Such a model may have low accuracy on both the training and testing data and is said to underfit the data. On the other hand, a model with high variance captures the noise or random fluctuations in the training data too closely. It may perform well on the training data but fails to generalize to unseen data, a phenomenon known as overfitting.

Here’s how the tradeoff works: When you have a simple model with low complexity, such as linear regression with few features or low polynomial degree, it generally exhibits high bias and low variance. As you increase the complexity, say by adding more features or increasing the polynomial degree, the model becomes more flexible and can better fit the training data. However, this comes at the cost of potentially higher variance and lower bias. The model becomes more prone to overfitting and may fail to generalize well to unseen data.

The goal is to strike a balance between bias and variance by finding an optimal level of model complexity that minimizes the total error. This is typically achieved through techniques like cross-validation, regularization, or ensemble methods that combine multiple models.

In summary, the bias-variance tradeoff refers to the tradeoff between a model’s bias and variance. Increasing complexity reduces bias but increases variance while decreasing complexity increases bias but reduces variance. The aim is to find the right level of complexity that minimizes the overall error and ensures good generalization to unseen data.

A great effort in attempting to explain the bias-variance tradeoff! If you have any further questions or if there’s anything you’d like me to clarify, please let me know before we move on to the next question

You can see how ChatGPT explained how bias-variance trade-off works under the hood and generated an even more detailed response to your incorrect answer.

Prompting Tips to Enhance Learning Experience

Whether you are preparing for an interview or creating a study plan, Here is a list of prompting tips that will allow you to make the most out of your learning experience with ChatGPT as your personalized tutor:

• Clearly state your learning objectives and be specific. For example, instead of asking, “Explain Data Preprocessing?” you can narrow down the scope and ask, “Can you please describe the process of feature selection in data preprocessing?”
• Ask follow-up questions to obtain further clarification. You can ask for additional examples or step-by-step explanations to better understand the concepts.
• Instead of a one-sided query conversation, create an interactive conversation with ChatGPT, enabling it to tailor the responses to your specific needs.
• To bridge the gap between theory and practice, prompt the ChatGPT and ask for practical examples and use cases.
• Ask for feedback and possible improvements as it facilitates continuous improvement.

Summary

ChatGPT is a valuable tool that you can use to create a personalized learning environment. It can adapt beautifully according to the progress of the learner. Here are the key takeaways from this chapter:

• ChatGPT can tailor the journey to address the strengths and weaknesses of the individual.
• Define your end goals and subgoals before drafting the initial prompt
• Follow the prompting tips for a more dynamic conversation and relevant responses

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