Pickl AI

MAY 30, 2024

Summary: The Data Science and Data Analysis life cycles are systematic processes crucial for uncovering insights from raw data. From acquisition to interpretation, these cycles guide decision-making, drive innovation, and enhance operational efficiency. billion INR by 2026, with a CAGR of 27.7%.

Data Analysis 52

Data Analysis Data Analysis Data Science Exploratory Data Analysis 52

Pickl AI

DECEMBER 14, 2023

From predicting patient outcomes to optimizing inventory management, these techniques empower decision-makers to navigate data landscapes confidently, fostering informed and strategic decision-making. It is a mathematical framework that aims to capture the underlying patterns, trends, and structures present in the data.

Data Analysis 52

Data Analysis Data Analysis Decision Trees Analytics 52

Towards AI

JULY 17, 2023

using PySpark we can run applications parallelly on the distributed cluster… blog.devgenius.io Pyspark MLlib is a wrapper over PySpark Core to do data analysis using machine-learning algorithms. We can find implementations of classification, clustering, linear regression, and other machine-learning algorithms in PySpark MLlib.

ML 98

ML ML Decision Trees Machine Learning 98

IBM Journey to AI blog

DECEMBER 20, 2023

Naïve Bayes algorithms include decision trees , which can actually accommodate both regression and classification algorithms. Random forest algorithms —predict a value or category by combining the results from a number of decision trees.

Machine Learning 92

Machine Learning Machine Learning Supervised Learning Clustering 92

Pickl AI

JANUARY 12, 2025

Introduction Are you struggling to decide between data-driven practices and AI-driven strategies for your business? Besides, there is a balance between the precision of traditional data analysis and the innovative potential of explainable artificial intelligence.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence AI AI 52

Dataconomy

JUNE 13, 2023

In this era of information overload, utilizing the power of data and technology has become paramount to drive effective decision-making. Decision intelligence is an innovative approach that blends the realms of data analysis, artificial intelligence, and human judgment to empower businesses with actionable insights.

Power BI 103

Power BI Data Analysis Data Analysis Artificial Intelligence 103

Towards AI

APRIL 4, 2024

A sector that is currently being influenced by machine learning is the geospatial sector, through well-crafted algorithms that improve data analysis through mapping techniques such as image classification, object detection, spatial clustering, and predictive modeling, revolutionizing how we understand and interact with geographic information.

K-nearest Neighbors 81

K-nearest Neighbors Machine Learning Machine Learning Decision Trees 81

Dataconomy

MAY 25, 2023

Here are some ways AI enhances IoT devices: Advanced data analysis AI algorithms can process and analyze vast volumes of IoT-generated data. By leveraging techniques like machine learning and deep learning, IoT devices can identify trends, anomalies, and patterns within the data.

Internet of Things 160

Internet of Things Artificial Intelligence Artificial Intelligence AI 160

Pickl AI

APRIL 3, 2023

Significantly, Supervised Learning is practical in two types of tasks- Classification: the goal is to predict a categorical label for each input data point Regression: the goal is to predict a continuous value. Significantly, there are two types of Unsupervised Learning: Clustering: which involves grouping similar data points together.

Supervised Learning 52

Supervised Learning Machine Learning Machine Learning Clustering 52

Pickl AI

OCTOBER 15, 2024

Summary: Statistical Modeling is essential for Data Analysis, helping organisations predict outcomes and understand relationships between variables. It encompasses various models and techniques, applicable across industries like finance and healthcare, to drive informed decision-making.

Decision Trees 52

Decision Trees Hypothesis Testing Clustering Data Analysis 52

IBM Journey to AI blog

DECEMBER 19, 2023

Machine learning algorithms for unstructured data include: K-means: This algorithm is a data visualization technique that processes data points through a mathematical equation with the intention of clustering similar data points.

Machine Learning 82

Machine Learning Machine Learning Supervised Learning K-nearest Neighbors 82

Pickl AI

JULY 12, 2024

Scikit-learn: A simple and efficient tool for data mining and data analysis, particularly for building and evaluating machine learning models. Data Normalization and Standardization: Scaling numerical data to a standard range to ensure fairness in model training.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Dataconomy

AUGUST 9, 2023

How could machine learning be used in network traffic analysis? Machine learning is fundamentally changing the landscape of network traffic analysis by automating the process of data analysis and interpretation.

Machine Learning 91

Machine Learning Machine Learning ML ML 91

ODSC - Open Data Science

AUGUST 15, 2023

Big Data Analysis with PySpark Bharti Motwani | Associate Professor | University of Maryland, USA Ideal for business analysts, this session will provide practical examples of how to use PySpark to solve business problems. Finally, you’ll discuss a stack that offers an improved UX that frees up time for tasks that matter.

Data Science 52

Data Science Machine Learning Machine Learning Data Scientist 52

Pickl AI

JULY 10, 2023

As a programming language it provides objects, operators and functions allowing you to explore, model and visualise data. The programming language can handle Big Data and perform effective data analysis and statistical modelling. R’s workflow support enhances productivity and collaboration among data scientists.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Pickl AI

APRIL 26, 2024

Top 50+ Interview Questions for Data Analysts Technical Questions SQL Queries What is SQL, and why is it necessary for data analysis? SQL stands for Structured Query Language, essential for querying and manipulating data stored in relational databases. What are the advantages and disadvantages of decision trees ?

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

Pickl AI

SEPTEMBER 12, 2024

Data Cleaning: Raw data often contains errors, inconsistencies, and missing values. Data cleaning identifies and addresses these issues to ensure data quality and integrity. Data Visualisation: Effective communication of insights is crucial in Data Science.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

phData

MAY 12, 2023

In today’s data-driven world, industries across the board are turning to advanced tools and technologies to gain deeper insights and improve their decision-making processes. This is particularly true in the financial services sector, where accurate, real-time data analysis can be the key to success.

Data Warehouse 72

Data Warehouse Machine Learning Machine Learning Database 72

Pickl AI

NOVEMBER 18, 2024

UnSupervised Learning Unlike Supervised Learning, unSupervised Learning works with unlabeled data. The algorithm tries to find hidden patterns or groupings in the data. Clustering and dimensionality reduction are common tasks in unSupervised Learning. Decision trees are easy to interpret but prone to overfitting.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Pickl AI

SEPTEMBER 3, 2023

Anomaly Detection in Machine Learning: An approach to data analysis and Machine Learning called “anomaly detection,” also referred to as “outlier detection,” focuses on finding data points or patterns that considerably differ from what is considered to be “normal” or anticipated behaviour.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Algorithm 52

Pickl AI

FEBRUARY 12, 2024

Statistical Concepts A strong understanding of statistical concepts, including probability, hypothesis testing, regression analysis, and experimental design, is paramount in Data Science roles. Clustering algorithms such as K-means and hierarchical clustering are examples of unsupervised learning techniques.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

Pickl AI

NOVEMBER 5, 2024

Companies can tailor products and services to individual preferences based on extensive Data Analysis. Machine Learning Expertise Familiarity with a range of Machine Learning algorithms is crucial for Data Science practitioners. Personalisation at Scale AI will enable hyper-personalization in marketing strategies.

Data Science 52

Data Science Artificial Intelligence Artificial Intelligence Machine Learning 52

Mlearning.ai

MAY 23, 2023

The following Venn diagram depicts the difference between data science and data analytics clearly: 3. Data analysis can not be done on a whole volume of data at a time especially when it involves larger datasets. Overfitting: The model performs well only for the sample training data.

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

Pickl AI

FEBRUARY 7, 2025

Unsupervised Learning Unsupervised learning involves training the algorithm on unlabeled data. The goal is to uncover hidden patterns or structures in the data. Clustering and anomaly detection are examples of unsupervised learning tasks. Its ability to learn from large volumes of data makes it ideal for complex applications.

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

Pickl AI

NOVEMBER 28, 2024

Decision Trees These trees split data into branches based on feature values, providing clear decision rules. Unsupervised Learning Unsupervised learning involves training models on data without labels, where the system tries to find hidden patterns or structures.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

JULY 18, 2023

While it may not be a traditional programming language, SQL plays a crucial role in Data Science by enabling efficient querying and extraction of data from databases. SQL’s powerful functionalities help in extracting and transforming data from various sources, thus helping in accurate data analysis.

Data Science 52

Data Science SQL Data Scientist Python 52

Pickl AI

SEPTEMBER 23, 2024

Summary: The blog explores the synergy between Artificial Intelligence (AI) and Data Science, highlighting their complementary roles in Data Analysis and intelligent decision-making. These components solve complex problems and drive decision-making in various industries.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Data Science Machine Learning 52

Heartbeat

SEPTEMBER 28, 2023

In a typical MLOps project, similar scheduling is essential to handle new data and track model performance continuously. Load and Explore Data We load the Telco Customer Churn dataset and perform exploratory data analysis (EDA). Are there clusters of customers with different spending patterns? #3.

Machine Learning 52

Machine Learning Machine Learning Support Vector Machines ML 52

DagsHub

APRIL 21, 2024

Scikit-learn Scikit-learn is a machine learning library in Python that is majorly used for data mining and data analysis. It offers implementations of various machine learning algorithms, including linear and logistic regression , decision trees , random forests , support vector machines , clustering algorithms , and more.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

DagsHub

SEPTEMBER 16, 2024

LIME can help improve model transparency, build trust, and ensure that models make fair and unbiased decisions by identifying the key features that are more relevant in prediction-making. LIME provides explanations for individual predictions by approximating the model locally with an interpretable model like a decision tree.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Dataconomy

MARCH 12, 2025

– An effective tool in clustering and classification tasks, enhancing the performance of group analysis. In machine learning: – Improves decision tree algorithms, particularly in the node-splitting phase, adding precision to predictions.

Hypothesis Testing 103

Hypothesis Testing Machine Learning Machine Learning Decision Trees 103