Addicted to Tech

Data Science v/s Artificial Intelligence v/s Machine Learning v/s Deep Learning What is Data Science? Let’s break the term into its composite parts – data and science.  Science works fundamentally through the formulation of hypotheses – educated guesses that seek to explain how something works and then finding enough reasonable evidence through observations in the real world to either prove the hypothesis right, or falsify it. Data, on the other hand, refers simply to numbers and statistics which we gather for the sake of analysis. By combining these two, we get data science. What exactly does it mean? Data science is an umbrella term for all techniques and methods that we use to analyze massive amounts of data with the purpose of extracting knowledge from them. Example of Data Science: Let’s say you are crazy about Cricket, which I am sure you are, and there is an ongoing series between India and Australia. India loses the first two matche...

Machine Learning Basic Doubts: What is Machine  Learning? Machine learning is a core sub-area of AI, it enables computers to get into  a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change and develop by themselves. To put simply, the iterative aspect of ML is the ability to adapt new data independently. This is possible as programs learn from previous computations and use "pattern recognition" to produce reliable results. Why Machine Learning? Consider some of the instances where ML is applied:the self-driving Google car, cyber fraud detection, online recommendation engines- like Fb's friend suggestion, Youtube's video recommendations, Netflix's movies/shows recommendations and "more item to consider" and get yourself a little something" on Amazon- are all examples of applied ML. Machine Learning has 2 phases : Learning Phas...

Data Science — including machine learning, data analysis, and data visualization First of all, let’s review what machine learning  is . I think the best way to explain what machine learning is would be to give you a simple example. Let’s say you want to develop a program that automatically detects what’s in a picture. So, given this picture below (Picture 1), you want your program to recognize that it’s a dog. Picture 1 Given this other one below (Picture 2), you want your program to recognize that it’s a table. Picture 2 You might say, well, I can just write some code to do that. For example, maybe if there are a lot of light brown pixels in the picture, then we can say that it’s a dog. Or maybe, you can figure out how to detect edges in a picture. Then, you might say, if there are many straight edges, then it’s a table. However, this kind of approach gets tricky pretty quickly. What if there’s a white dog in the picture with no brown hai...