Towards AI

FEBRUARY 20, 2024

If you want an overview of the Machine Learning Process, it can be categorized into 3 wide buckets: Collection of Data: Collection of Relevant data is key for building a Machine learning model. It isn't easy to collect a good amount of quality data. You need to know two basic terminologies here, Features and Labels.

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ODSC - Open Data Science

NOVEMBER 13, 2023

For previous grant performance, you can tap into online databases, which offer historical data on funded projects and their outcomes. According to a report by Gartner, poor data quality costs businesses an average of $12.9 million , emphasizing the importance of relying on reputable sources.

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Pickl AI

NOVEMBER 18, 2024

The article also addresses challenges like data quality and model complexity, highlighting the importance of ethical considerations in Machine Learning applications. Key steps involve problem definition, data preparation, and algorithm selection. Data quality significantly impacts model performance.

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DrivenData Labs

APRIL 13, 2023

Some winning models benefitted from treating regions differently, even fitting separate models on each region's data. For example, the first place model fit a decision tree using satellite imagery for the midwest and northeast, but did not use satellite imagery for the south and west where they found data quality was lower.

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Pickl AI

JULY 12, 2024

This section explores the essential steps in preparing data for AI applications, emphasising data quality’s active role in achieving successful AI models. Importance of Data in AI Quality data is the lifeblood of AI models, directly influencing their performance and reliability.

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Pickl AI

OCTOBER 15, 2024

They identify patterns in existing data and use them to predict unknown events. Techniques like linear regression, time series analysis, and decision trees are examples of predictive models. Data Collection and Preparation The first and most critical step in building a Statistical Model is gathering and preparing the data.

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ODSC - Open Data Science

FEBRUARY 11, 2025

Building Real-World Applications: Lessons andMistakes Chip Huyen candidly shared common mistakes she has observed in AI application development: Overengineering: Many teams rush to use generative AI for tasks that simpler methods, such as decision trees, could handle more effectively. Focus on data quality over quantity.

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Pickl AI

SEPTEMBER 12, 2024

Key Components of Data Science Data Science consists of several key components that work together to extract meaningful insights from data: Data Collection: This involves gathering relevant data from various sources, such as databases, APIs, and web scraping.

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Pickl AI

MAY 30, 2024

This crucial stage involves data cleaning, normalisation, transformation, and integration. By addressing issues like missing values, duplicates, and inconsistencies, preprocessing enhances data quality and reliability for subsequent analysis. Data Cleaning Data cleaning is crucial for data integrity.

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Iguazio

JUNE 25, 2023

Here are some best practices that can help you ensure your model is reliable and accurate: Ensure the Quality of Input Data Continuously monitor the quality of the input data being fed into the model. If the data quality deteriorates, it can adversely impact the model's performance.

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Pickl AI

DECEMBER 16, 2024

Foundational techniques like decision trees, linear regression , and neural networks lay the groundwork for solving various problems. Limited Access to High-Quality Data Data is the lifeblood of AI, yet many organisations struggle to access clean, reliable, and diverse datasets.

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Mlearning.ai

NOVEMBER 29, 2023

Decision Trees ML-based decision trees are used to classify items (products) in the database. This is the applied machine learning algorithm that works with tabular and structured data. In its core, lie gradient-boosted decision trees. Obviously, this one is best for commercial analyses.

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Defined.ai blog

AUGUST 10, 2023

Meanwhile, ML is the mechanism that enables the AI to learn from the data, improve over time, and make more accurate predictions. For instance, regression algorithms in Machine Learning are widely employed to predict stock prices based on historical data. Data Quality For AI to produce reliable results, it needs high-quality data.

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Pickl AI

JANUARY 3, 2024

Feature Engineering enhances model performance, and interpretability, mitigates overfitting, accelerates training, improves data quality, and aids deployment. Feature Engineering is the art of transforming raw data into a format that Machine Learning algorithms can comprehend and leverage effectively.

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Pickl AI

JUNE 6, 2024

However, raw data is often messy and needs cleaning and transformation to be usable. Model Building & Training Once the data is ready, data scientists choose appropriate algorithms like regression analysis, decision trees, or machine learning techniques. Can Predictive Modeling Predict Human Behavior Perfectly?

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Pickl AI

OCTOBER 3, 2024

Various models can be used depending on the nature of the data and the specific goals of the analysis. Decision Trees : Help visualise decisions and their possible consequences. Model Training In this phase, historical data is used to train the selected model.

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Pickl AI

AUGUST 9, 2024

Data Cleaning and Transformation Techniques for preprocessing data to ensure quality and consistency, including handling missing values, outliers, and data type conversions. Students should learn about data wrangling and the importance of data quality.

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Mlearning.ai

AUGUST 20, 2023

Photo by Bruno Nascimento on Unsplash Introduction Data is the lifeblood of Machine Learning Models. The data quality is critical to the performance of the model. The better the data, the greater the results will be. Before we feed data into a learning algorithm, we need to make sure that we pre-process the data.

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Dataconomy

MARCH 13, 2023

Here are some of the best practices for collecting high-quality data: Data relevance:  Collect data that is relevant to the problem at hand. Data quality:  Ensure that the data is accurate, complete, and free from errors. How to improve your data quality in four steps?

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Pickl AI

APRIL 26, 2024

What are the advantages and disadvantages of decision trees ? Advantages: It is easy to interpret and visualise, can handle numerical and categorical data, and requires fewer data preprocessing. Describe a situation where you had to think creatively to solve a data-related challenge.

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Pickl AI

JULY 18, 2023

It supports the handling of large and complex data sets from different sources, including databases, spreadsheets, and external files. SAS allows users to merge, join, and manipulate data easily, ensuring data quality and consistency. It offers tools for data exploration, ad-hoc querying, and interactive reporting.

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DataRobot Blog

MAY 26, 2022

However, with the widespread adoption of modern ML techniques, including gradient-boosted decision trees (GBDTs) and deep learning algorithms , many traditional validation techniques become difficult or impossible to apply.

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The MLOps Blog

NOVEMBER 14, 2023

It’s also much more difficult to see how the intricate network of neurons processes the input data than to comprehend, say, a decision tree. By inspecting the features that are activated incorrectly or inconsistently, we can refine the training process or identify data quality issues.

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Pickl AI

NOVEMBER 28, 2024

Decision Trees These trees split data into branches based on feature values, providing clear decision rules. Team Collaboration ML engineers must work closely with Data Scientists to ensure data quality and with engineers to integrate models into production.

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Pickl AI

JULY 23, 2023

Data Modeling: Developing predictive models using machine learning algorithms like regression, decision trees, and neural networks. Data Cleansing: Ensuring data quality and removing outliers to improve model accuracy. Key Features: i.

Analytics 52

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Chatbots Life

SEPTEMBER 11, 2023

It encompasses a wide range of tasks, including noun phrase extraction, part-of-speech tagging, sentiment analysis, and classification using algorithms like Naive Bayes and Decision Tree. Training data quality and bias: ML-based grammar checkers heavily rely on training data to learn patterns and make predictions.

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Heartbeat

MAY 29, 2023

The weak models can be trained using techniques such as decision trees or neural networks, and the outputs are combined using techniques such as weighted averaging or gradient boosting. LLMs require a large amount of data to be trained and fine-tuned, and managing this data is critical to the success of the deployment.

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DagsHub

SEPTEMBER 16, 2024

Source: [link] Moreover, visualizing input and output data distributions helps assess the data quality and model behavior. 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.

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Dataconomy

APRIL 22, 2025

Time series analysis: Analyzes data points collected or recorded at specific time intervals. Decision trees: Visual representations that aid in decision-making processes. The success of these models relies heavily on data quality and the selection of appropriate analytical tools.

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Dataconomy

DECEMBER 12, 2024

Download volume and data quality were controlled by parameters within the program. Some of our test data was collected manually to ensure that there were different files, but we were unable to collect sufficient quantities of every file types, so in many cases automatic downloads remained the primary method of generating input.

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