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Principle component Analysis  * Application of PCA: 1. Dimensionality reduction - Explain with one example 2. Data Visualization - we cant visualize beyond 3D so if we want to visualize multidimensional data say 10 Dimensional data then we can use PCA. 3. Data Anonymization - if you don't want to send any confidential information then you can use PCA. give an example of credit card fraud detection. https://www.kaggle.com/mlg-ulb/creditcardfraud 4. Factor Analysis: PCA component is basically a linear combination of multiple features so you can use it for factor analysis. How PCA does dimension reduction. If I take any point then I need two values (x1 and x2) to represent this point. that means this is 2 dimensional. Now how can I represent this same data on only 1 dimension without losing any information? We can do this by rotating the axis. if I rotate my axis like this then I can see our all points are lying on the only one axis so we have preserved our all the information on...

1. Hello Everyone, my name is .... and I am the Mentor of this batch. 2. Explain what we are going to do:     1. This session will be 2 and hours and in this, we'll learn in more detail what we have learnt through the recorded videos. since we have a small batch it is a good opportunity for us to introduce each other. 1. Introduce yourself 2. ask for the introduction to others.     1. I would like to know you guys so please tell me your name, domain or industry you are working in, your purpose of learning data science and if you want to share your experience or anything we would love to hear you. so, please take less than a minute and introduce yourself. I have a list of people who are in this meeting.  the first name, I can see is ____ so ___ can you introduce yourself? "say few lines about what he said" like this very interesting, welcome to the class. glad to have you. if someone is not responding or not able to hear his voice then say no problem I'll come ba...

1. Introduce yourself. 2. get an intro from the learners. you only take their names and ask for their Name, their Experience and what did you decide to join. 3. say something inspirational in their response, like this sounds super interesting or anything in your experience. 4. Personalize the mentoring and examples you give according to the industries and needs of the batch. 5. The tone of the first session is very important because people expect things based on the first session.  6. use people names. 7. learners interact with you based on how you interact with them. if you are confident then they will also be confident.  8. we are here to give a personalised experience to mentor based on our industry experience. 9. The goal is not to teach the concepts from scratch, the students have already learnt them in the video. rather the goal is to recap the concepts and provide a quick overview before the hands-on part. 10. approach from the industry perspective and always give examp...

* What learners are expecting? 1. ability to work with data:  2. apply knowledge learn: how to use learnt knowledge in solving the real-world problem. when to use, when not to use, where to use and solve real-world problems. 3. interpret output: should be able to interpret the output and should be able to tell the story of the data and output and insides. 4. ability to solve the problem: for given problems by using data at hand. how do you approach the problem? * How to deliver the session? 1. Explain the agenda. understand the learners and things to focus on. (10 mins) 2. Clarify doubts and prioritize the topic based on the majority of learners. (20 mins) 3. hands-on. if time taking then prepare the output in the advance. (60 mins) 4. Extended doubt clearing: make it interactive, you only ask questions and take answers from learners based on use cases. (25 mins) 5. ask learners to summarize and you fill in the gaps (5 mins) * best practices for delivering a good session: 1. Prepar...