Data Science Dojo

JULY 15, 2024

decision trees, support vector regression) that can model even more intricate relationships between features and the target variable. Support Vector Machines (SVM): This algorithm finds a hyperplane that best separates data points of different classes in high-dimensional space.

Machine Learning 307

Machine Learning Machine Learning Algorithm Clustering 307

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

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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

IBM Journey to AI blog

DECEMBER 20, 2023

They’re also part of a family of generative learning algorithms that model the input distribution of a given class or/category. Naïve Bayes algorithms include decision trees , which can actually accommodate both regression and classification algorithms.

Machine Learning 92

Machine Learning Machine Learning Supervised Learning Clustering 92

PyImageSearch

JANUARY 23, 2023

The reasoning behind that is simple; whatever we have learned till now, be it adaptive boosting, decision trees, or gradient boosting, have very distinct statistical foundations which require you to get your hands dirty with the math behind them. The goal is to nullify the abstraction created by packages as much as possible.

Deep Learning 101

Deep Learning Deep Learning Algorithm Decision Trees 101

Mlearning.ai

FEBRUARY 15, 2024

Data Science Project — Predictive Modeling on Biological Data Part III — A step-by-step guide on how to design a ML modeling pipeline with scikit-learn Functions. Photo by Unsplash Earlier we saw how to collect the data and how to perform exploratory data analysis. Now comes the exciting part ….

Data Science 52

Data Science Decision Trees Exploratory Data Analysis ML 52

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. Scikit-learn: A simple and efficient tool for data mining and data analysis, particularly for building and evaluating machine learning models.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

ODSC - Open Data Science

AUGUST 15, 2023

You’ll explore the current production-grade tools, techniques, and workflows as well as explore the 8 layers of the machine learning stack. You’ll get hands-on practice with unsupervised learning techniques, such as K-Means clustering, and classification algorithms like decision trees and random forest.

Data Science 52

Data Science Machine Learning Machine Learning Data Scientist 52

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

Defined.ai blog

AUGUST 10, 2023

Businesses must understand how to implement AI in their analysis to reap the full benefits of this technology. In the following sections, we will explore how AI shapes the world of financial data analysis and address potential challenges and solutions.

Business Intelligence 52

Business Intelligence Business Intelligence Data Analysis Data Analysis 52

PyImageSearch

DECEMBER 12, 2022

Setting Up the Prerequisites Building the Model Assessing the Model Summary Citation Information Scaling Kaggle Competitions Using XGBoost: Part 2 In our previous tutorial , we went through the basic foundation behind XGBoost and learned how easy it was to incorporate a basic XGBoost model into our project. Table 1: The Dataset.

Decision Trees 52

Decision Trees Deep Learning Deep Learning Exploratory Data Analysis 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

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

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

Machine Learning Machine Learning Data Science Big Data 70

Pickl AI

NOVEMBER 2, 2023

A cheat sheet for Data Scientists is a concise reference guide, summarizing key concepts, formulas, and best practices in Data Analysis, statistics, and Machine Learning. Here, we’ll explore why Data Science is indispensable in today’s world. Is Data Scientist math heavy?

Data Scientist 52

Data Scientist Data Science Data Visualization 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

IBM Journey to AI blog

DECEMBER 19, 2023

The main difference being that while KNN makes assumptions based on data points that are closest together, LOF uses the points that are furthest apart to draw its conclusions. Unsupervised learning Unsupervised learning techniques do not require labeled data and can handle more complex data sets.

Machine Learning 82

Machine Learning Machine Learning Supervised Learning K-nearest Neighbors 82

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

DECEMBER 12, 2024

Introduction Boosting is a powerful Machine Learning ensemble technique that combines multiple weak learners, typically decision trees, to form a strong predictive model. Lets explore the mathematical foundation, unique enhancements, and tree-pruning strategies that make XGBoost a standout algorithm. Lower values (e.g.,

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

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.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 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

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. link] Ganaie, M.

Cross Validation 52

Cross Validation Decision Trees Algorithm Natural Language Processing 52

Pickl AI

NOVEMBER 28, 2024

For example, in neural networks, data is represented as matrices, and operations like matrix multiplication transform inputs through layers, adjusting weights during training. Without linear algebra, understanding the mechanics of Deep Learning and optimisation would be nearly impossible.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

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

Artificial Intelligence Artificial Intelligence AI AI 91

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.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 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. Batch size and learning rate are two important hyperparameters that can significantly affect the training of deep learning models, including LLMs.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78

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. An ensemble of decision trees is trained on both normal and anomalous data. Anomalies might lead to deviations from the normal patterns the model has learned.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Algorithm 52

Heartbeat

FEBRUARY 8, 2023

Python packages such as Scikit-learn assist fundamental machine learning algorithms such as classification and regression, whereas Keras, Caffe, and TensorFlow enable deep learning. R Source: i2tutorials Statisticians developed R as a tool for statistical computing. We pay our contributors, and we don’t sell ads.

Natural Language Processing 52

Natural Language Processing Machine Learning Machine Learning Deep Learning 52

DagsHub

APRIL 21, 2024

Moving the machine learning models to production is tough, especially the larger deep learning models as it involves a lot of processes starting from data ingestion to deployment and monitoring. It provides different features for building as well as deploying various deep learning-based solutions.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

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

Machine Learning Machine Learning Support Vector Machines ML 52

DagsHub

SEPTEMBER 16, 2024

Source: [link] Weights and Biases Weights and biases are the key components of the deep learning architectures that affect the model performance. 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.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Dataconomy

MARCH 25, 2025

Tabular data is a foundational element in the realm of data analysis, serving as the backbone for a variety of machine learning applications. What is tabular data? Tabular data consists of structured information organized in rows and columns, resembling a spreadsheet layout.

Deep Learning 91

Deep Learning Deep Learning Decision Trees Machine Learning 91

Dataconomy

MARCH 27, 2025

The rise of neural networks in the 1980s marked a pivotal shift, driven by advancements in deep learning techniques. Understanding the neural vs. symbolic paradigm Neural methods, like deep learning, excel in pattern recognition and are adept at processing large datasets quickly.

AI 125

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