Sanchit Aggarwal @SanchitAgg8353

1) apply() - The pandas #apply() function operates on both dataframes & series. We can use it on either columns of dataframes (axis=1) or on rows of dataframes (axis=0)

✅Day 8⃣ of #Deeplearning🧬 ▫️ Topic - Customer Churn #Prediction Using #ANN 🔰 📝Customer #churn refers to phenomenon of customers discontinuing their relationship with a business or brand It plays a crucial role in customer retention strategies & business growth ⚔️

✅Perceptron algorithm aims to minimize this hinge loss function by iteratively updating weights of decision boundary based on misclassified examples in training set. 🔰Fantastic Blog - quora.com/What-is-the-lo….

✅Here's a simplified explanation of Perceptron #Trick Initialization: Start with random weights for each input. Make a Prediction: For a given input, calculate weighted sum of inputs using current weights & apply a threshold to get perceptron's prediction (0 or 1)

▫️ Applications of RBF Neural Network- Function approximation: RBF are effective in approximating complex mathematical function Pattern recognition: can be used for face, fingerprint, & character recognition Time series prediction: RBF networks can capture temporal dependency

3) Radial Basis Function (#RBF) Neural Network - is a feedforward neural network that uses radial basis function as activation functions consist of multiple layers, including an input layer, one or more hidden layers with radial basis activation functions, and an output layer

▫️ The challenge with #LSTM networks lies in selecting appropriate architecture & parameters & dealing with vanishing or exploding gradients during training Applications - Natural language processing Speech recognition Time series analysis

4) Artificial Neural Network (#ANN) - A single perceptron (or neuron) can be imagined as a Logistic Regression ANN is a group of multiple perceptrons/ neurons at each layer is also known as a Feed-Forward Neural network because inputs are processed only in forward direction

5) Convolution Neural Network (#CNN) especially prevalent in image & video processing projects The building blocks of CNNs are filters a.k.a. kernels. #Kernels are used to extract relevant features from input using convolution operation

Somewhere in Noida 😅 #random

The three main types of learning in neural networks are supervised learning, unsupervised learning, and reinforcement learning Blog - analyticsvidhya.com/blog/2020/02/c…

Hello friends, so today onwards i will share content related to #deeplearning 😀 #DataScience

▶️Why do we need a DBSCAN when we already have K-means clustering ? K-Means clustering may cluster loosely related observations together. Every observation becomes a part of some cluster eventually, even if the observations are scattered far away in the vector space

▫️ It comprises of many methods based on diff distance measures. E.g. K-Means (distance between points), Affinity propagation (graph distance), Mean-shift (distance between points) DBSCAN (distance between nearest points), Gaussian mixtures (#Mahalanobis distance to centers)

▶️DBSCAN #algorithm uses two parameters: minPts: minimum number of points (a threshold) clustered together for a region to be considered dense eps (ε): A distance measure that will be used to locate points in neighborhood of any point.

✅#DataAnalyst Project on Shark Tank @kaggle Project Link - kaggle.com/code/sachinkum…

▫️ Boosting is an ensemble-based learning algorithm that converts weak learn learners to strong estimators which involves training ML models sequentially one after other wherein in each iteration model tries to correct error made by model in previous iteration

✅np.arange() - Argument start: starting value of sequence (inclusive). If omitted, it defaults to 0 stop:end value of sequence (exclusive) step:spacing btwn values in sequenc(default is 1). It can be positive or negative dtype:Optional data type for elements of the array

✅100 Days of #MachineLearning 🎰 ▫️ Demystifying Machine Learning : Unraveling the Magic Behind Smart Algorithms in Plain Language!" 🖇️Repo Link for #100DaysofML - github.com/sachinkumar160…

1) #Haversine Formula It’s used to calculate shortest distance between two points on Earth’s surface. It provides good accuracy for small distances.