ajay mehta
Stories (18/0)
Unleashing the Power of Artificial Intelligence: From Sci-Fi Dreams to Real-World Impact
Introduction: In the realm of technological advancements, few innovations have captivated the human imagination quite like artificial intelligence (AI). It's a concept that has stirred curiosity and sparked countless debates, as we ponder the implications of machines possessing human-like intelligence. From the silver screens of Hollywood to the forefront of scientific research, AI has come a long way, becoming an indispensable part of our daily lives. In this article, we'll dive into the enthralling world of AI, exploring its incredible capabilities and pondering its profound impact on our future.
By ajay mehta10 months ago in Futurism
Detecting and Preventing Data Leakage in Machine Learning:
WHY? Data scientists typically follow a process where they start by taking a dataset and perform a train-test-split. The purpose of this split is to divide the data into training and testing sets. The training data is used to train the model, while the testing data is used to evaluate its performance. Let's assume that after training and testing, the data scientist achieves an accuracy of 95%, which is considered good. Now, when the project is deployed and users start using it, the data scientist notices that the accuracy rarely exceeds 70%.
By ajay mehta10 months ago in Education
"Exploring Bernoulli Naive Bayes and Unveiling the Power of Out-of-Core Learning"
Before moving forward, you must have understood of below 2 posts. post 1 Post 2 Agenda: Now we are going to discuss the logic behind the Bernoulli Naive Bayes. Additionally, we will also cover learning out-of-core Naive Bayes.
By ajay mehta10 months ago in Education
"Demystifying Naïve Bayes: Simple yet Powerful for Text Classification"
Naïve Bayes Naïve Bayes is, first of all, a classification algorithm that helps us solve classification-related problems. Not only does it assist in classification, but it particularly excels in analyzing textual data. If you are working with text or need to perform tasks like spam filtering or sentiment analysis, applying Naïve Bayes as a baseline model is highly recommended before exploring other algorithms, such as deep learning.
By ajay mehta10 months ago in Education
"Mastering Regularization Techniques: Enhancing Model Performance and Generalization"
Regularization is a technique used in machine learning to prevent overfitting, which occurs when a model becomes too complex and performs well on the training data but poorly on new, unseen data. It helps to find a balance between capturing the patterns in the training data and generalizing well to new data.
By ajay mehta11 months ago in Education
"Demystifying Principal Component Analysis: A Comprehensive Guide"
In simple terms, PCA (Principal Component Analysis) is a technique used to simplify and understand complex data. It takes a dataset with many variables and finds the most important patterns or trends in the data.
By ajay mehta11 months ago in Education
"Unraveling the Power of K-Nearest Neighbors: A Comprehensive Guide to the KNN Algorithm"
K-Nearest Neighbors (KNN) K-Nearest Neighbors (KNN) is a simple, yet powerful machine learning algorithm used for both classification and regression tasks. It makes predictions based on the similarity of input data to its neighboring data points.
By ajay mehta11 months ago in Education
"Mastering ROC AUC Curve: A Comprehensive Guide for Data Scientists"
Why ROC Curve? practical implementation after completing blog. In supervised machine learning, we encounter two types of problems: regression and classification. In a regression problem, we aim to predict a numerical value, such as predicting the salary (LPA) based on features like CGPA and IQ. On the other hand, in a classification problem, we seek to predict the class or category to which a data point belongs. For example, determining whether a student is placed or not based on their CGPA and IQ.
By ajay mehta11 months ago in Education
"Understanding Kurtosis and How to Determine if Your Data has a Normal Distribution."
What is Kurtosis? Kurtosis is a statistical term that measures the degree of peaked Ness or flatness of a distribution compared to the normal distribution. A distribution with high kurtosis has a sharp peak and fat tails, indicating that it has a higher probability of extreme values than a normal distribution. On the other hand, a distribution with low kurtosis has a flatter peak and thinner tails, indicating that it has a lower probability of extreme values than a normal distribution.
By ajay mehta11 months ago in Education
"Unlocking the Power of Chi Square: A Guide to Statistical Analysis"
Chi Square Distribution The Chi-Square distribution, also written as χ² distribution, is a continuous probability distribution that is widely used in statistical hypothesis testing, particularly in the context of goodness-of-fit tests and tests for independence in contingency tables. It arises when the sum of the squares of independent standard normal random variables follows this distribution.
By ajay mehta11 months ago in Education