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Day 15 of learning AI/ML as a beginner. Topic: computer science introduction. As I have posted yesterday that I think I have erroneously omitted some fundamental topics which I realised just when I was about to learn word3vec. I asked you all to give me some advice and guide me through and some amazing people really guided me on how should I approach learning AI/ML and some even shared their own roadmap to help me. Based on those suggestions I have decided to start learning some computer science topics which may not necessarily be used right know but can help in the long run (as some amazing people suggested). I really hope I am not going on wrong tracks again (please guide me through if I am). First I have learn about binary (base-2) which only consists of two number 0 and 1 which represent off and on respectively. These binary digits are called bits and there are 8 bits present in a byte now each byte can be used to represent 255 characters (256 if 0 is included). This is quite enough to represent English language. The American Standard Code for Information Interchange (ASCII) has formulated some patterns to represent different characters since there was an overlap between the numbers and characters representations. "A" for example is represented by number 65 (in binary 01000001) and other alphabets in increasing order like 66 -> B, 67 -> C etc. ASCII has a predefined table for which number represent what like 33 represent "!". Then there's Unicode which can be used to represent Numbers, Alphabets, Special characters, Colors, Images, Videos and even Sound. Because there are a number of pixels present in a screen which contains RGB color combination in the form of binary (these can be same as the number to represent characters however they are also used to represent color combinations - this depends on the software you are using to decode those binary as text or colors). The image is made up of colors and from many images is made a video. Binary combinations can also be used to represent sound. Then there's algorithms which are a predefined step-by-step set of instructions to solve a problem. Algorithm speed can be pictured into three big O notations n = this the the slow and most inefficient algorithm as it uses more time and have to perform many steps to solve the problem. n/2 = this is twice as fast then the previous one however it will also require to perform more steps if the problem is increased. log2n = this is usually the fastest and most efficient algorithm as it only needs to do just one more step if the problem is increased. Also here are my handwritten notes and I am open for suggestions and recommendations as well. And do you think I should post these as "Day x of learning CS for AI/ML as a beginner"?