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.

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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.

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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.

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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. Isolation forest: This type of anomaly detection algorithm uses unsupervised data.

Machine Learning 82

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ODSC - Open Data Science

JANUARY 5, 2024

Without this library, data analysis wouldn’t be the same without pandas, which reign supreme with its powerful data structures and manipulation tools. Pandas provides a fast and efficient way to work with tabular data. It is widely used in data science, finance, and other fields where data analysis is essential.

Python 52

Python K-nearest Neighbors Data Science Data Visualization 52

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. TensorFlow and Keras: TensorFlow is an open-source platform for machine learning. classification, regression) and data characteristics.

Artificial Intelligence 52

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Pickl AI

JANUARY 29, 2024

Key Components In Data Science, key components include data cleaning, Exploratory Data Analysis, and model building using statistical techniques. ML focuses on algorithms like decision trees, neural networks, and support vector machines for pattern recognition. billion by 2029.

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Pickl AI

FEBRUARY 12, 2024

Machine Learning Algorithms Candidates should demonstrate proficiency in a variety of Machine Learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks. Here is a brief description of the same.

Data Science 52

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Towards AI

JULY 19, 2023

That post was dedicated to an exploratory data analysis while this post is geared towards building prediction models. Feel free to try other algorithms such as Random Forests, Decision Trees, Neural Networks, etc., Motivation The motivating question is— ‘What are the chances of survival of a heart failure patient?’.

Machine Learning 40

Machine Learning Machine Learning K-nearest Neighbors Support Vector Machines 40

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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IBM Journey to AI blog

JULY 6, 2023

Machine learning can then “learn” from the data to create insights that improve performance or inform predictions. Just as humans can learn through experience rather than merely following instructions, machines can learn by applying tools to data analysis.

Machine Learning 70

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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). Random Forest Classifier (rf): Ensemble method combining multiple decision trees.

Machine Learning 52

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Pickl AI

NOVEMBER 18, 2024

Selecting an Algorithm Choosing the correct Machine Learning algorithm is vital to the success of your model. For example, linear regression is typically used to predict continuous variables, while decision trees are great for classification and regression tasks. Decision trees are easy to interpret but prone to overfitting.

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Dataconomy

FEBRUARY 24, 2023

It could be anything from customer service to data analysis. Collect data: Gather the necessary data that will be used to train the AI system. This data should be relevant, accurate, and comprehensive. Several algorithms are available, including decision trees, neural networks, and support vector machines.

Artificial Intelligence 91

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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

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Pickl AI

FEBRUARY 7, 2025

Algorithms Used in Both Fields In Machine Learning, algorithms focus on learning from labelled data to make predictions or decisions. Common algorithms include Linear Regression, Decision Trees, Random Forests, and Support Vector Machines.

Deep Learning 52

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Pickl AI

SEPTEMBER 3, 2023

49% of companies in the world that use Machine Learning and AI in their marketing and sales processes apply it to identify the prospects of sales. Anomalies might have low probabilities under the fitted GMM, as they deviate from the common Gaussian patterns observed in normal data.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Algorithm 52

Mlearning.ai

FEBRUARY 15, 2023

Some important things that were considered during these selections were: Random Forest : The ultimate feature importance in a Random forest is the average of all decision tree feature importance. A random forest is an ensemble classifier that makes predictions using a variety of decision trees. Cambridge: MIT Press.

Cross Validation 52

Cross Validation Algorithm Decision Trees Natural Language Processing 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

Pickl AI

NOVEMBER 28, 2024

Decision Trees These trees split data into branches based on feature values, providing clear decision rules. Support Vector Machines (SVM) SVMs are powerful classifiers that separate data into distinct categories by finding an optimal hyperplane.

Machine Learning 52

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Dataconomy

SEPTEMBER 1, 2023

So how can the technology of our time, machine learning, be used to improve the quality and length of human life? Heart disease stands as one of the foremost global causes of mortality today, presenting a critical challenge in clinical data analysis. Dealing with missing values is a common challenge in medical data analysis.

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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. Scikit-learn provides a consistent API for training and using machine learning models, making it easy to experiment with different algorithms and techniques.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Dataconomy

MARCH 28, 2025

Decision trees are a fundamental tool in machine learning, frequently used for both classification and regression tasks. Their intuitive, tree-like structure allows users to navigate complex datasets with ease, making them a popular choice for various applications in different sectors. What is a decision tree?

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