Pickl AI

NOVEMBER 4, 2024

This article will guide you through effective strategies to learn Python for Data Science, covering essential resources, libraries, and practical applications to kickstart your journey in this thriving field. Key Takeaways Python’s simplicity makes it ideal for Data Analysis. in 2022, according to the PYPL Index.

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Data Science Dojo

AUGUST 28, 2023

Some projects may necessitate a comprehensive LLMOps approach, spanning tasks from data preparation to pipeline production. Exploratory Data Analysis (EDA) Data collection: The first step in LLMOps is to collect the data that will be used to train the LLM.

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Towards AI

AUGUST 16, 2023

We can apply a data-centric approach by using AutoML or coding a custom test harness to evaluate many algorithms (say 20–30) on the dataset and then choose the top performers (perhaps top 3) for further study, being sure to give preference to simpler algorithms (Occam’s Razor).

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Dataconomy

AUGUST 16, 2023

Data scientists are the master keyholders, unlocking this portal to reveal the mysteries within. They wield algorithms like ancient incantations, summoning patterns from the chaos and crafting narratives from raw numbers. Model development : Crafting magic from algorithms!

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Towards AI

FEBRUARY 3, 2024

From Predicting the behavior of a customer to automating many tasks, Machine learning has shown its capacity to convert raw data into actionable insights. Even though converting raw data into actionable insights, it is not determined by ML algorithms alone. This process is called Exploratory Data Analysis(EDA).

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Pickl AI

DECEMBER 3, 2024

In Python, commonly used libraries include: Pandas: For data manipulation and analysis, particularly for handling structured data. Scikit-learn: For Machine Learning algorithms and preprocessing utilities. Matplotlib/Seaborn: For data visualization. During EDA, you can: Check for missing values.

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Becoming Human

MAY 15, 2023

These communities will help you to be updated in the field, because there are some experienced data scientists posting the stuff, or you can talk with them so they will also guide you in your journey. Data Analysis After learning math now, you are able to talk with your data.

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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.

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Mlearning.ai

FEBRUARY 28, 2023

Theoretical Explanations and Practical Examples of Correlation between Categorical and Continuous Values Without any doubt, after obtaining the dataset, giving entire data to any ML model without any data analysis methods such as missing data analysis, outlier analysis, and correlation analysis.

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Ocean Protocol

MARCH 11, 2024

METAR, Miami International Airport (KMIA) on March 9, 2024, at 15:00 UTC In the recently concluded data challenge hosted on Desights.ai , participants used exploratory data analysis (EDA) and advanced artificial intelligence (AI) techniques to enhance aviation weather forecasting accuracy.

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Towards AI

FEBRUARY 21, 2023

First of all, HR needs to collect comprehensive data about an employee, such as education, salary, experience… We also need data from supervisors such as performance, relationships, promotions… After that, HR can use this information to predict employees’ tendency to leave and take preventive action. TRAIN ==Staying Rate: 83.87%Leaving

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Pickl AI

JANUARY 3, 2024

Feature engineering in machine learning is a pivotal process that transforms raw data into a format comprehensible to algorithms. Through Exploratory Data Analysis , imputation, and outlier handling, robust models are crafted. Time features Objective: Extracting valuable information from time-related data.

Machine Learning 52

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Mlearning.ai

MARCH 22, 2023

Principal Component Analysis(PCA) is an essential algorithm in a data scientist's toolkit. This makes it particularly useful for analyzing large datasets with many variables, where it can be difficult to visualize and interpret the data. This shows the data will likely be classified using linear algorithms.

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Ocean Protocol

MAY 24, 2024

The challenge required a detailed analysis of Google Trends data, integration of additional data sources, and the application of advanced ML methods to predict market behaviors. Data scientists across various expertise levels engaged in this challenge to determine Google Trends’ impact on cryptocurrency valuations.

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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 Visualization: Matplotlib, Seaborn, Tableau, etc.

Data Engineering 52

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Ocean Protocol

DECEMBER 12, 2023

Abstract This research report encapsulates the findings from the Curve Finance Data Challenge , a competition that engaged 34 participants in a comprehensive analysis of the decentralized finance protocol. Part 1: Exploratory Data Analysis (EDA) MEV Over 25,000 MEV-related transactions have been executed through Curve.

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Ocean Protocol

SEPTEMBER 9, 2024

F1 :: 2024 Strategy Analysis Poster ‘The Formula 1 Racing Challenge’ challenges participants to analyze race strategies during the 2024 season. They will work with lap-by-lap data to assess how pit stop timing, tire selection, and stint management influence race performance.

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Pickl AI

JULY 12, 2024

Jupyter notebooks are widely used in AI for prototyping, data visualisation, and collaborative work. Their interactive nature makes them suitable for experimenting with AI algorithms and analysing data. Importance of Data in AI Quality data is the lifeblood of AI models, directly influencing their performance and reliability.

Artificial Intelligence 52

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DrivenData Labs

APRIL 10, 2024

In the Kelp Wanted challenge, participants were called upon to develop algorithms to help map and monitor kelp forests. Winning algorithms will not only advance scientific understanding, but also equip kelp forest managers and policymakers with vital tools to safeguard these vulnerable and vital ecosystems.

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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

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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.

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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

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Pickl AI

DECEMBER 16, 2024

Summary: AI in Time Series Forecasting revolutionizes predictive analytics by leveraging advanced algorithms to identify patterns and trends in temporal data. Advanced algorithms recognize patterns in temporal data effectively. Making Data Stationary: Many forecasting models assume stationarity.

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AWS Machine Learning Blog

JULY 31, 2023

Model-centric approach In this approach, the data always remains the same and is used to iteratively improve the model to meet desired results. We use the model preview functionality to perform an initial EDA.

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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

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The MLOps Blog

MAY 31, 2023

The ML platform can utilize historic customer engagement data, also called “clickstream data”, and transform it into features essential for the success of the search platform. From an algorithmic perspective, Learning To Rank (LeToR) and Elastic Search are some of the most popular algorithms used to build a Seach system.

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Towards AI

MARCH 1, 2023

Business questions to brainstorm: Since all features are anonymous, we will focus our analysis on non-anonymized features: Time, Amount How different is the amount of money used in different transaction classes? Exploratory Data Analysis — EDA Let us now check the missing values in the dataset.

Machine Learning 52

Machine Learning Machine Learning Python Exploratory Data Analysis 52

ODSC - Open Data Science

AUGUST 9, 2023

Understanding the Session In this engaging and interactive session, we will delve into PySpark MLlib, an invaluable resource in the field of machine learning, and explore how various classification algorithms can be implemented using AWS Glue/EMR as our platform. But this session goes beyond just concepts and algorithms.

Machine Learning 52

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Mlearning.ai

JUNE 16, 2023

GPT-4 Data Pipelines: Transform JSON to SQL Schema Instantly Blockstream’s public Bitcoin API. The data would be interesting to analyze. From Data Engineering to Prompt Engineering Prompt to do data analysis BI report generation/data analysis In BI/data analysis world, people usually need to query data (small/large).

AI 52

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AWS Machine Learning Blog

APRIL 6, 2023

We also demonstrate the performance of our state-of-the-art point cloud-based product lifecycle prediction algorithm. Challenges One of the challenges we faced while using fine-grained or micro-level modeling like product-level models for sale prediction was missing sales data.

Machine Learning 89

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Ocean Protocol

OCTOBER 18, 2024

Provided information included telemetry data covering each race, including variables like tire choices, stint lengths, lap times, and pit stop durations. Each participant performed Exploratory Data Analysis (EDA) to uncover relationships between variables like tire degradation and race performance.

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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.

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Ocean Protocol

JANUARY 10, 2024

Here we use data science to diagnose the issues and propose better practices to treat our planet better than the last 30 years. Exploratory Data Analysis (EDA) In Asia, the surge in CO2 and GHG emissions is closely linked to rapid population growth, industrialization, and the rise of emerging economies.

Data Science 52

Data Science Exploratory Data Analysis Support Vector Machines Data Analysis 52

Heartbeat

MAY 29, 2023

It is therefore important to carefully plan and execute data preparation tasks to ensure the best possible performance of the machine learning model. It is also essential to evaluate the quality of the dataset by conducting exploratory data analysis (EDA), which involves analyzing the dataset’s distribution, frequency, and diversity of text.

Machine Learning 78

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PyImageSearch

DECEMBER 12, 2022

With the completion of AdaBoost, we are one more step closer to understanding the XGBoost algorithm. load the data in the form of a csv estData = pd.read_csv("/content/realtor-data.csv") # drop NaN values from the dataset estData = estData.dropna() # split the labels and remove non-numeric data y = estData["price"].values

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Heartbeat

MARCH 22, 2023

In this article, let’s dive deep into the Natural Language Toolkit (NLTK) data processing concepts for NLP data. Before building our model, we will also see how we can visualize this data with Kangas as part of exploratory data analysis (EDA). This, in turn, reduces the time complexity and space.

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Dataconomy

AUGUST 5, 2024

For instance: “Data Consultant bot is designed to assist you with all your data analysis needs. Whether you’re looking to interpret complex datasets, forecast trends, or gain insights from your data, this bot provides expert guidance and practical solutions. This is how others will get to know your bot.

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