Data science

Machine Learning Model Multiple Linear Regression .

descriptive statistics is one that describes the population. On the other end, Inferential statistics are used to make the generalization about the population based on the samples. Here's a difference to make it easier for you to understand. Save the pin and Read our blog to know more about the statistics. #statistics #assignmenthelp #BLOG #dissertation

Generalized Linear Models (GLM) extend the ordinary linear regression and allow the response variable y to have an error distribution other than the normal distribution. GLMs are: a) Easy to Understand b) Simple to fit and interpret in any statistical package c) Sufficient in a lot of practical application 1. Linear Regression 2. Logistic Regression 3. Poisson Regression Source: Marketing Distillery

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Daniel Lee shared insights on neural network activation functions, sparking 37-comment discussion on AI model optimization. The post delves into crucial elements shaping neural network behavior and performance, revealing community engagement.#NeuralNetworks, #ActivationFunctions, #AIML, #DeepLearning, #AIInsights

10 Big Data Trends - Changing the Face of Business #bigdata #infographic:

K-Means Clustering (day 43)

Analytics Vidhya | Data Science Community on Instagram: "Unveiling the Mysteries of Linear Regression 📈 Linear Regression - A cornerstone in the world of machine learning! Often, it's our first step into predictive modeling, teaching us the intricacies of relationship dynamics between variables. But did you know that even this foundational algorithm comes with its own set of hyperparameters? While simple linear regression strives for simplicity, regularized variations like Ridge or Lasso...

How to Become More Marketable as a Data Scientist

What is #gradientdescent in Machine Learning? Gradient descent is a fundamental optimisation algorithm. It is used in #machinelearning to minimise a given cost function by iteratively adjusting the parameters of a #model.

Data preprocessing is a crucial step in the data analysis pipeline that involves cleaning and transforming raw data into a format suitable for analysis