Pickl AI

OCTOBER 14, 2024

Exploratory Data Analysis (EDA) Exploratory Data Analysis (EDA) is an approach to analyse datasets to uncover patterns, anomalies, or relationships. The primary purpose of EDA is to explore the data without any preconceived notions or hypotheses. Clustering: Grouping similar data points to identify segments within the data.

Data Analysis 52

Data Analysis Data Analysis EDA Data Mining 52

Pickl AI

JUNE 7, 2023

Face Recognition One of the most effective Github Projects on Data Science is a Face Recognition project that makes use of Deep Learning and Histogram of Oriented Gradients (HOG) algorithm. You can make use of HOG algorithm for orientation gradients and use Python library for creating and viewing HOG representations.

Data Science 52

Data Science Deep Learning Deep Learning Clustering 52

Becoming Human

MAY 15, 2023

For Data Analysis you can focus on such topics as Feature Engineering , Data Wrangling , and EDA which is also known as Exploratory Data Analysis. First learn the basics of Feature Engineering, and EDA then take some different-different data sheets (data frames) and apply all the techniques you have learned to date.

Data Science 75

Data Science Machine Learning Machine Learning Database 75

Ocean Protocol

SEPTEMBER 9, 2024

By conducting exploratory data analysis (EDA), they will identify relationships between these variables and generate insights on how strategy impacts race outcomes. Participants will use EDA and statistical analysis to understand how tire management and pit stop decisions impact race outcomes.

EDA 45

EDA Exploratory Data Analysis Hypothesis Testing Data Science 45

Pickl AI

FEBRUARY 12, 2024

Technical Proficiency Data Science interviews typically evaluate candidates on a myriad of technical skills spanning programming languages, statistical analysis, Machine Learning algorithms, and data manipulation techniques. Differentiate between supervised and unsupervised learning algorithms. Here is a brief description of the same.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

Mlearning.ai

MAY 23, 2023

This includes skills in data cleaning, preprocessing, transformation, and exploratory data analysis (EDA). Blind 75 LeetCode Questions - LeetCode Discuss Data Manipulation and Analysis Proficiency in working with data is crucial. Familiarity with libraries like pandas, NumPy, and SQL for data handling is important.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Pickl AI

SEPTEMBER 12, 2024

A Algorithm: A set of rules or instructions for solving a problem or performing a task, often used in data processing and analysis. Clustering: An unsupervised Machine Learning technique that groups similar data points based on their inherent similarities.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Pickl AI

JULY 25, 2023

With expertise in Python, machine learning algorithms, and cloud platforms, machine learning engineers optimize models for efficiency, scalability, and maintenance. They possess a deep understanding of statistical methods, programming languages, and machine learning algorithms.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Pickl AI

JULY 20, 2023

Techniques like regression analysis, time series forecasting, and machine learning algorithms are used to predict customer behavior, sales trends, equipment failure, and more. Kaggle datasets) and use Python’s Pandas library to perform data cleaning, data wrangling, and exploratory data analysis (EDA).

Analytics 40

Analytics Analytics Big Data Big Data 40

The MLOps Blog

DECEMBER 28, 2022

EDA, as it is popularly called, is the pivotal phase of the project where discoveries are made. Team collaboration Its team composition presents a great case wherein they have emphasized building robust data and model pipelines, such as the capacity expansion of prediction clusters, refining codebase, and retraining models.

ML 78

ML ML Data Scientist Machine Learning 78

Heartbeat

JANUARY 25, 2023

By analyzing the words and phrases used in a piece of writing, sentiment analysis algorithms can determine the overall sentiment of the text and provide a more complete understanding of its meaning. Now you need to perform some EDA and cleaning on the data after loading it into the notebook.

Python 52

Python Deep Learning Deep Learning ML 52

DrivenData Labs

DECEMBER 7, 2023

Solvers submitted a wide range of methodologies to this end, including using open-source and third party LLMs (GPT, LLaMA), clustering (DBSCAN, K-Means), dimensionality reduction (PCA), topic modeling (LDA, BERT), sentence transformers, semantic search, named entity recognition, and more. and DistilBERT. What motivated you to participate? :

Natural Language Processing 130

Natural Language Processing Clustering Data Science Data Analysis 130