Pickl AI

JULY 10, 2023

Hence, you can use R for classification, clustering, statistical tests and linear and non-linear modelling. It provides functions for descriptive statistics, hypothesis testing, regression analysis, time series analysis, survival analysis, and more. How is R Used in Data Science?

Data Science 52

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Pickl AI

SEPTEMBER 12, 2024

Clustering: An unsupervised Machine Learning technique that groups similar data points based on their inherent similarities. D Data Mining : The process of discovering patterns, insights, and knowledge from large datasets using various techniques such as classification, clustering, and association rule learning.

Data Analyst 52

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Pickl AI

MARCH 20, 2024

Proficiency in probability distributions, hypothesis testing, and statistical modelling enables Data Scientists to derive actionable insights from data with confidence and precision. Mastery of statistical concepts equips professionals to make informed decisions and draw accurate conclusions from empirical observations.

Data Science 52

Data Science Data Scientist Data Wrangling Machine Learning 52

Pickl AI

APRIL 3, 2025

Modeling & Algorithms: Applying statistical models (like regression, classification, clustering) or Machine Learning algorithms to identify deeper patterns, make predictions, or classify data points. Modeling: Build a logistic regression or decision tree model to predict the likelihood of a customer churning based on various factors.

Data Analysis 52

Data Analysis Data Analysis Data Visualization EDA 52

Pickl AI

NOVEMBER 28, 2024

Concepts such as probability distributions, hypothesis testing , and Bayesian inference enable ML engineers to interpret results, quantify uncertainty, and improve model predictions. Decision Trees These trees split data into branches based on feature values, providing clear decision rules.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

MAY 31, 2023

Begin by employing algorithms for supervised learning such as linear regression , logistic regression, decision trees, and support vector machines. After that, move towards unsupervised learning methods like clustering and dimensionality reduction. It includes regression, classification, clustering, decision trees, and more.

Data Science 52

Data Science Python Data Scientist Machine Learning 52

Pickl AI

AUGUST 9, 2024

Some of the most notable technologies include: Hadoop An open-source framework that allows for distributed storage and processing of large datasets across clusters of computers. Statistical Analysis Introducing statistical methods and techniques for analysing data, including hypothesis testing, regression analysis, and descriptive statistics.

Big Data 52

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Pickl AI

NOVEMBER 5, 2024

Statistical Knowledge A solid understanding of statistics is fundamental for analysing data distributions and conducting hypothesis testing. Mastery of these tools allows Data Scientists to efficiently process large datasets and develop robust models.

Data Science 52

Data Science Artificial Intelligence Artificial Intelligence Machine Learning 52

Pickl AI

MAY 30, 2024

It’s critical in harnessing data insights for decision-making, empowering businesses with accurate forecasts and actionable intelligence. Options include linear regression for continuous outcomes and decision trees for classification tasks. The choice impacts the model’s performance and accuracy.

Data Analysis 52

Data Analysis Data Analysis Data Science Exploratory Data Analysis 52

Mlearning.ai

MAY 23, 2023

There are majorly two categories of sampling techniques based on the usage of statistics, they are: Probability Sampling techniques: Clustered sampling, Simple random sampling, and Stratified sampling. Decision trees are more prone to overfitting. Some algorithms that have low bias are Decision Trees, SVM, etc.

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

Pickl AI

APRIL 26, 2024

Then, I would use clustering techniques such as k-means or hierarchical clustering to group customers based on similarities in their purchasing behaviour. What are the advantages and disadvantages of decision trees ? You’re tasked with predicting sales for a retail store. What approach would you take?

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

Pickl AI

SEPTEMBER 23, 2024

Hypothesis testing and regression analysis are crucial for making predictions and understanding data relationships. Machine Learning Supervised Learning includes algorithms like linear regression, decision trees, and support vector machines.

Artificial Intelligence 52

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