Reality of Practical Impletation of ML Projects

After completing two initial ML courses, which I did reasonably comfortably, I wanted to make a very basic project my self based on what I learnt. I experienced that there are a number of problems like set up data, enviroment creation etc is required to be solved, which are readily available for exercises to be completed in such tutorials which I comleted. So it is utmost important to be able to create basic set up and environment creation other than data preparation for the project you are working on. Data preparation is making digital representation of your project in np array. If you are building an image compression project, you have to convert your png or jpg image into matrix of intensities of pixels etc. and than converting them in to required formats. Some of the imported libraries do not work at all as I am unable to run run_kMeans() So I am stuck up here and decided to first practice those small things before attempting to take on the third course. I am enjoying the course content. I feel I will enjoy more if I practice the basics to get more clearer ideas about what's happening under the hood. I quote Prof Andrew Ng "It is completely fine to start small, and use that to learn and continue to grow! " I am taking a break for 15-20 days for my neurological checkup in hospital. Then I will get back to the ML and share my learning experience. Until next week BYE!

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