Dataconomy

JULY 28, 2023

In the digital age, the abundance of textual information available on the internet, particularly on platforms like Twitter, blogs, and e-commerce websites, has led to an exponential growth in unstructured data. Text data is often unstructured, making it challenging to directly apply machine learning algorithms for sentiment analysis.

Power BI 91

Power BI Data Preparation Exploratory Data Analysis Machine Learning 91

Mlearning.ai

MAY 23, 2023

Blind 75 LeetCode Questions - LeetCode Discuss Data Manipulation and Analysis Proficiency in working with data is crucial. This includes skills in data cleaning, preprocessing, transformation, and exploratory data analysis (EDA).

Data Science 52

Data Science Data Scientist Apache Hadoop Machine Learning 52

Pickl AI

JULY 18, 2023

By transitioning from computer science to data science, you can tap into a broader range of job opportunities and potentially increase your earning potential. Leveraging existing skills: Computer science provides a strong foundation in programming, algorithms, and problem-solving, which are highly valuable in data science.

Computer Science 52

Computer Science Computer Science Data Science Machine Learning 52

Pickl AI

APRIL 26, 2024

I would perform exploratory data analysis to understand the distribution of customer transactions and identify potential segments. Then, I would use clustering techniques such as k-means or hierarchical clustering to group customers based on similarities in their purchasing behaviour. What approach would you take?

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

Pickl AI

JULY 25, 2023

Collaborating with data scientists, to ensure optimal model performance in real-world applications. With expertise in Python, machine learning algorithms, and cloud platforms, machine learning engineers optimize models for efficiency, scalability, and maintenance.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Pickl AI

JULY 20, 2023

Predictive Analytics Projects: Predictive analytics involves using historical data to predict future events or outcomes. Techniques like regression analysis, time series forecasting, and machine learning algorithms are used to predict customer behavior, sales trends, equipment failure, and more.

Analytics 40

Analytics Analytics Big Data Big Data 40

Pickl AI

JULY 17, 2023

Machine Learning is a subset of artificial intelligence (AI) that focuses on developing models and algorithms that train the machine to think and work like a human. It entails developing computer programs that can improve themselves on their own based on expertise or data. What is Unsupervised Machine Learning?

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Clustering 52

IBM Journey to AI blog

DECEMBER 20, 2023

That’s why diversifying enterprise AI and ML usage can prove invaluable to maintaining a competitive edge. Each type and sub-type of ML algorithm has unique benefits and capabilities that teams can leverage for different tasks. Here, we’ll discuss the five major types and their applications. What is machine learning?

Machine Learning 83

Machine Learning Machine Learning Supervised Learning Clustering 83

Towards AI

APRIL 19, 2024

Originally published on Towards AI. An idea of Text Analysis. An entire statistical analysis of those entities in the dataset should be carried out. Finally, specific algorithms should run on top of that analysis. It’s an open-source Python package for Exploratory Data Analysis of text.

Analytics 89

Analytics Analytics Data Analysis Data Analysis 89

Pickl AI

JULY 10, 2023

The programming language can handle Big Data and perform effective data analysis and statistical modelling. R allows you to conduct statistical analysis and offers capabilities of statistical and graphical representation. How is R Used in Data Science?

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Becoming Human

MAY 15, 2023

There is a position called Data Analyst whose work is to analyze the historical data, and from that, they will derive some KPI s (Key Performance Indicators) for making any further calls. For Data Analysis you can focus on such topics as Feature Engineering , Data Wrangling , and EDA which is also known as Exploratory Data Analysis.

Data Science 75

Data Science Machine Learning Machine Learning Database 75

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