In this episode, I have discussed the syllabus to be covered in this podcast. The syllabus discussed here is sufficient enough to develop a great understanding of this field. However, you have to promise yourself to give at least 2 hours daily to study by yourself. These two hours will help you to develop your understanding of the concepts  ..read more

This episode is all about why I have started this podcast.  ..read more

In this episode, I explained the concept of regularization in layman's language to develop a clear understanding of it and its relation with bias and variance. It is recommended to listen to the previous episode (Bias and Variance) first.  ..read more

This episode discusses the difference between Normalization and Standardization. I have also discussed which method we should use based on the use case and ML algorithms. If you have not listened to the episodes on mean standard deviation and variance, then please listen to them first. Link to my website to check out the formulas ..read more

This episode discusses the interview questions based on the topic of Regularization in machine learning. The Link to my website ---->  Link ..read more

Interviewing for a job in the machine learning field can be nerve-wracking, especially when it comes to technical questions. But what if I told you that you have the power to steer the interview in a direction that plays to your strengths? In this episode, we're diving into the tips and tricks for controlling the interview and showcasing your knowledge to potential employers. मशीन लर्निंग क्षेत्र में नौकरी के लिए साक्षात्कार नर्व-ब्रेकिंग हो सकता है, खासकर जब तकनीकी प्रश्नों की बात आती है। लेकिन क्या होगा अगर मैंने आपको बताया कि आपके पास साक्षात्कार को उस दिशा में ले जाने की शक्ति है|   ..read more

Welcome to our latest episode.  Today, we are going to discuss one of the most fundamental concepts in machine learning - Bias and Variance. Understanding the concept of bias and variance is crucial for developing effective machine learning models. These two parameters help us to measure the accuracy of our predictions and understand how to improve our models. We will explain these concepts in detail and show how to balance them for optimal results. So, sit back, relax, and get ready to learn about one of the most important topics in machine learning ..read more

This episode discusses the orthogonal and orthonormal properties of vectors ..read more

This episode explains the concept behind the Histogram and Probability distribution along with the comparison between them. Please use this Link to check the example plot of histogram and probability distribution ..read more

In this episode I talked about the definition, meaning, significance, and limitation of the metric called: Covariance ..read more