Pickl AI

JULY 12, 2024

Summary: This guide explores Artificial Intelligence Using Python, from essential libraries like NumPy and Pandas to advanced techniques in machine learning and deep learning. Introduction Artificial Intelligence (AI) transforms industries by enabling machines to mimic human intelligence.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Mlearning.ai

MAY 23, 2023

Mathematics for Machine Learning and Data Science Specialization Proficiency in Programming Data scientists need to be skilled in programming languages commonly used in data science, such as Python or R. These languages are used for data manipulation, analysis, and building machine learning models.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 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

Towards AI

JULY 19, 2023

This is part 2, and you will learn how to do sales prediction using Time Series. Please refer to Part 1– to understand what is Sales Prediction/Forecasting, the Basic concepts of Time series modeling, and EDA I’m working on Part 3 where I will be implementing Deep Learning and Part 4 where I will be implementing a supervised ML model.

Cross Validation 98

Cross Validation Clustering EDA Data Preparation 98

Pickl AI

JULY 25, 2023

Proficient in programming languages like Python or R, data manipulation libraries like Pandas, and machine learning frameworks like TensorFlow and Scikit-learn, data scientists uncover patterns and trends through statistical analysis and data visualization. Data Visualization: Matplotlib, Seaborn, Tableau, etc.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Pickl AI

JUNE 7, 2023

Using Netflix user data, you need to undertake Data Analysis for running workflows like EDA, Data Visualisation and interpretation. Customer Segmentation using K-Means Clustering One of the most crucial uses of data science is customer segmentation. You will need to use the K-clustering method for this GitHub data mining project.

Data Science 52

Data Science Deep Learning Deep Learning Clustering 52

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

APRIL 3, 2025

Exploratory Data Analysis (EDA): Using statistical summaries and initial visualisations (yes, visualisation plays a role within analysis!) Modeling & Algorithms: Applying statistical models (like regression, classification, clustering) or Machine Learning algorithms to identify deeper patterns, make predictions, or classify data points.

Data Analysis 52

Data Analysis Data Analysis Data Visualization EDA 52

Heartbeat

SEPTEMBER 28, 2023

Hands-on Project Why customer churn matters and how to predict it with machine learning, explained step-by-step Photo by Gabrielle Ribeiro on Unsplash Introduction In today’s competitive business environment, retaining customers is essential to a company’s success. Follow “Nhi Yen” for future updates! Our project uses Comet ML to: 1.

Machine Learning 52

Machine Learning Machine Learning Support Vector Machines ML 52

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. Explain the bias-variance tradeoff in Machine Learning. Here is a brief description of the same.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

Pickl AI

SEPTEMBER 12, 2024

By understanding crucial concepts like Machine Learning, Data Mining, and Predictive Modelling, analysts can communicate effectively, collaborate with cross-functional teams, and make informed decisions that drive business success. Data Cleaning: Raw data often contains errors, inconsistencies, and missing values.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Pickl AI

MAY 30, 2024

Developing predictive models using Machine Learning Algorithms will be a crucial part of your role, enabling you to forecast trends and outcomes. Also Read: Explore data effortlessly with Python Libraries for (Partial) EDA: Unleashing the Power of Data Exploration. Use statistical methods to identify and remove these anomalies.

Data Analysis 52

Data Analysis Data Analysis Data Science Exploratory Data Analysis 52

AWS Machine Learning Blog

MAY 30, 2023

Since its introduction, we have helped hundreds of customers optimize their workloads, set guardrails, and improve the visibility of their machine learning (ML) workloads’ cost and usage. In this series of posts, we share lessons learned about optimizing costs in Amazon SageMaker. For example, ml.t2.medium

AWS 96

AWS ML ML EDA 96

The MLOps Blog

DECEMBER 28, 2022

Building ML team Following the surge in ML use cases that have the potential to transform business, the leaders are making a significant investment in ML collaboration, building teams that can deliver the promise of machine learning. EDA, as it is popularly called, is the pivotal phase of the project where discoveries are made.

ML 78

ML ML Data Scientist Machine Learning 78

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

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

Mlearning.ai

FEBRUARY 28, 2023

Exploratory Data Analysis (EDA) Univariate EDA Price: The price of a used car is the target variable and has a highly skewed distribution, with a median value of around 53.5 Bivariate EDA Contrary to intuition, Kilometers_Driven does not seem to have a relationship with the price. Both histograms are slightly right skewed.

Python 52

Python EDA Exploratory Data Analysis Data Analysis 52

Heartbeat

JANUARY 25, 2023

Build and deploy your own sentiment classification app using Python and Streamlit Source:Author Nowadays, working on tabular data is not the only thing in Machine Learning (ML). In this tutorial, you will see Machine Learning based approach using LSTM to build the sentiment analysis model.

Python 52

Python Deep Learning Deep Learning ML 52

Pickl AI

SEPTEMBER 23, 2024

Feature Extraction : PCA helps identify and extract the most influential features from a dataset, which can improve the performance of Machine Learning models. PCA is also commonly used in exploratory Data Analysis (EDA) when the aim is to detect patterns and relationships between variables before building more complex models.

Data Analysis 52

Data Analysis Data Analysis Exploratory Data Analysis EDA 52

DrivenData Labs

DECEMBER 7, 2023

In the Unsupervised Wisdom Challenge , participants were tasked with identifying novel, effective methods of using unsupervised machine learning to extract insights about older adult falls from narrative medical record data. Solution format. However, it wasn't the use of sophisticated tools alone that made for strong submissions.

Natural Language Processing 130

Natural Language Processing Clustering Data Science Data Analysis 130

Mlearning.ai

JULY 29, 2023

In this article, we present a comprehensive overview of the most commonly used data visualization functions and tools, with a particular focus on their applications in machine learning projects, especially those involving computer vision. What is data visualization? jpg") depth_raw = o3d.io.read_image("00000.png")

Python 52

Python Data Visualization Data Science Exploratory Data Analysis 52