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Introduction:  In the realm of artificial intelligence (AI), the quest to replicate human intelligence has been both awe-inspiring and challenging. While tremendous strides have been made in AI research and development, the gap between artificial and human intelligence remains substantial. Despite the remarkable advancements in machine learning algorithms, neural networks, and computational power, AI still grapples with fundamental aspects of human cognition. In this exploration, we delve into the intricacies of human intelligence and dissect the limitations that impede AI from rivaling its human counterparts. Human Intelligence: Human intelligence is a multifaceted phenomenon, encompassing cognition, perception, creativity, emotion, and social interaction. At the core of human intelligence lies a complex interplay of neural networks, biochemical processes, and environmental influences. The human brain, with its billions of neurons interconnected through intricate synaptic pathways...

 As we know machine learning deals with thousands of statistical-algorithms a choosing the right algorithm for the task is always a tedious process. But even after having the algorithms in hand understanding which category of machine learning is need for the task is also needs understanding. So here are different categories in machine learning. Supervised machine learning. Unsupervised machine learning. Semi supervised machine learning. Reinforcement machine learning. Supervised Machine Learning So Supervised Machine Learning is the process in which the algorithm is trained on the labeled dataset. Here label means each input datapoint is associated with the corresponding output or label. The term supervised means that during the training process the algorithm is provided with supervision in the form of labeled examples from which the algorithm could learn. If we break this thing in this way we could understand it better, Learning means the process of training a machine learning mo...

 This article delves into the examination of user reviews and ratings on Flipkart. These reviews serve as a valuable resource for informing others about their experiences and, importantly, provide insights into product quality and brand reputation. Through this analysis, we aim to offer users valuable information about products and suggest ways to improve product quality. Here we will be applying Machine Learning to analyze the data and make prediction, either if a product review is positive or negative. Before going into the analysis and code, you can download the data from this  link . Since we are using python here we will be using libraries & module like Pandas, Seaborne, Matplotlib, Scikit, NLTK etc. From the coding perspective i am using Jupyter Notebook which best for analysis and machine learning tasks. So first we will import our required modules and libraries in jupyter. Now if you have downloaded the dataset from the given link , import it using pandas and use t...

So before we get into what is data science let us first understand what is data actually, and how it is important for business, e-Commerce, for security, for identity of someone, even for scientific purpose or research and for even much more. So data is nothing but a piece of information , the information that we are collecting could be anything it can be your date of birth, your body weight, your eyes or hair colour, your meal list, what you are searching in your mobile or computer, the places you visit, so we can say anything around you either connected to you or around you can be data.  But if someone is novice he will ask, how all these things can be data ? Answer is Data is everywhere but what type of data is our need and which type of data is not our need makes the all difference. Lets understand this clearly through an example- Suppose you want to do some shopping on Amazon, and you decided to buy a new mobile phone, you fixed the budget, then features that you want in the t...