Data Science Dojo

JUNE 1, 2023

Machine Learning with TensorFlow by Google AI This is a beginner-level course that teaches you the basics of machine learning using TensorFlow , a popular machine-learning library. The course covers topics such as linear regression, logistic regression, and decision trees.

Machine Learning 195

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Towards AI

APRIL 7, 2024

Created by the author with DALL E-3 Statistics, regression model, algorithm validation, Random Forest, K Nearest Neighbors and Naïve Bayes— what in God’s name do all these complicated concepts have to do with you as a simple GIS analyst? You just want to create and analyze simple maps not to learn algebra all over again.

Machine Learning 92

Machine Learning Machine Learning K-nearest Neighbors Supervised Learning 92

Towards AI

JANUARY 26, 2024

Examples of hyperparameters for algorithms Advantages and Disadvantages of hyperparameter tuning How to perform hyperparameter tuning?– For example, in the training of deep learning models, the weights and biases can be considered as model parameters. However, sometimes we do need to provide the initial values for them.

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K-nearest Neighbors Deep Learning Deep Learning Algorithm 89

Mlearning.ai

FEBRUARY 15, 2024

Let’s use those fancy algorithms to make predictions from our data. There are many algorithms which can be used from this task ranging from Logistic regression to Deep learning. Later we will use another algorithm as well to see if we can further improve the result. You can refer part-I and part-II of this article.

Data Science 52

Data Science Decision Trees Exploratory Data Analysis Cross Validation 52

Pickl AI

AUGUST 1, 2023

In this blog, we will delve into the fundamental concepts of data model for Machine Learning, exploring their types. What is Machine Learning? Examples of supervised learning models include linear regression, decision trees, support vector machines, and neural networks. regression, classification, clustering).

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Machine Learning Machine Learning Support Vector Machines Decision Trees 52

The MLOps Blog

NOVEMBER 14, 2023

Deep learning models are typically highly complex. While many traditional machine learning models make do with just a couple of hundreds of parameters, deep learning models have millions or billions of parameters. The reasons for this range from wrongly connected model components to misconfigured optimizers.

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ODSC - Open Data Science

SEPTEMBER 18, 2023

A lot goes into learning a new skill, regardless of how in-depth it is. Getting started with natural language processing (NLP) is no exception, as you need to be savvy in machine learning, deep learning, language, and more.

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Analytics Vidhya

DECEMBER 30, 2022

It involves developing algorithms and models to analyze, understand, and generate human language, enabling computers to perform sentiment analysis, language translation, text summarization, and tasks. Natural language processing (NLP) is […].

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Natural Language Processing Analytics Analytics Computer Science 236

Dataconomy

OCTOBER 5, 2023

AI practitioners choose an appropriate machine learning model or algorithm that aligns with the problem at hand. Common choices include neural networks (used in deep learning), decision trees, support vector machines, and more. Another form of machine learning algorithm is known as unsupervised learning.

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AI AI Artificial Intelligence Artificial Intelligence 136

Towards AI

APRIL 14, 2023

In the second part, I will present and explain the four main categories of XML algorithms along with some of their limitations. However, typical algorithms do not produce a binary result but instead, provide a relevancy score for which labels are the most appropriate. Thus tail labels have an inflated score in the metric.

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K-nearest Neighbors Algorithm Clustering Support Vector Machines 92

Dataconomy

MAY 25, 2023

On the other hand, artificial intelligence is the simulation of human intelligence in machines that are programmed to think and learn like humans. By leveraging advanced algorithms and machine learning techniques, IoT devices can analyze and interpret data in real-time, enabling them to make informed decisions and take autonomous actions.

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ODSC - Open Data Science

AUGUST 15, 2023

You’ll get hands-on practice with unsupervised learning techniques, such as K-Means clustering, and classification algorithms like decision trees and random forest. Finally, you’ll explore how to handle missing values and training and validating your models using PySpark.

Machine Learning 52

Machine Learning Machine Learning Data Science Data Scientist 52

Dataconomy

AUGUST 9, 2023

This is where the power of machine learning (ML) comes into play. Machine learning algorithms, with their ability to recognize patterns, anomalies, and trends within vast datasets, are revolutionizing network traffic analysis by providing more accurate insights, faster response times, and enhanced security measures.

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IBM Journey to AI blog

DECEMBER 19, 2023

This type of machine learning is useful in known outlier detection but is not capable of discovering unknown anomalies or predicting future issues. Local outlier factor (LOF ): Local outlier factor is similar to KNN in that it is a density-based algorithm.

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Machine Learning Machine Learning Supervised Learning K-nearest Neighbors 70

Pickl AI

NOVEMBER 2, 2023

These reference guides condense complex concepts, algorithms, and commands into easy-to-understand formats. Expertise in mathematics and statistical fields is essential for deciding algorithms, drawing conclusions, and making predictions. Machine Learning Machine learning is at the heart of Data Science.

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Dataconomy

MARCH 13, 2023

With advances in machine learning, deep learning, and natural language processing, the possibilities of what we can create with AI are limitless. Key concepts of AI The following are some of the key concepts of AI: Data:  AI requires vast amounts of data to learn and improve its performance over time.

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Artificial Intelligence Artificial Intelligence Natural Language Processing Algorithm 98

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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Data Science Cross Validation Machine Learning Machine Learning 52

Heartbeat

SEPTEMBER 13, 2023

Through the explainability of AI systems, it becomes easier to build trust, ensure accountability, and enable humans to comprehend and validate the decisions made by these models. For example, explainability is crucial if a healthcare professional uses a deep learning model for medical diagnoses.

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Smart Data Collective

SEPTEMBER 16, 2020

Predictive analytics, sometimes referred to as big data analytics, relies on aspects of data mining as well as algorithms to develop predictive models. These statistical models are growing as a result of the wide swaths of available current data as well as the advent of capable artificial intelligence and machine learning.

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Predictive Analytics Analytics Analytics Decision Trees 122

Mlearning.ai

APRIL 4, 2023

Instead of traditional segmentation based on hard business metrics, financial institutions can group customers into smaller micro-segments using fine-tuned ML algorithms. Conclusion In short, ML algorithms enable lenders to get meaningful insights on the expected losses in case of default, based on historical and current information.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

IBM Journey to AI blog

JULY 6, 2023

Just as humans can learn through experience rather than merely following instructions, machines can learn by applying tools to data analysis. Machine learning works on a known problem with tools and techniques, creating algorithms that let a machine learn from data through experience and with minimal human intervention.

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Machine Learning Machine Learning Data Science Big Data 58

phData

AUGUST 24, 2023

With a modeled estimation of the applicant’s credit risk, lenders can make more informed decisions and reduce the occurrence of bad loans, thereby protecting their bottom line. Greater Accuracy Machine learning models can handle high-dimensional, nonlinear, and interactive relationships between variables. loan default or not).

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ML ML Machine Learning Machine Learning 52

Pickl AI

JULY 14, 2023

Evolution of AI The evolution of Artificial Intelligence (AI) spans several decades and has witnessed significant advancements in theory, algorithms, and applications. Techniques such as decision trees, support vector machines, and neural networks gained popularity.

Artificial Intelligence 52

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Towards AI

APRIL 11, 2023

Classification In Classification, we use an ML Algorithm to classify the digit based on its features. The algorithm can be trained on a dataset of labeled digit images, which allows it to learn to recognize the patterns in the images. Artificial Neural Networks (ANNs) are machine learning models that can be used for HDR.

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Machine Learning Machine Learning Support Vector Machines Decision Trees 94

Pickl AI

NOVEMBER 8, 2023

Versatility: From classification to regression, Scikit-Learn Cheat Sheet covers a wide range of Machine Learning tasks. Decision Tree) Making Predictions Evaluating Model Accuracy (Classification) Feature Scaling (Standardization) Getting Started Before diving into the intricacies of Scikit-Learn, let’s start with the basics.

Machine Learning 52

Machine Learning Machine Learning Data Science Python 52

Smart Data Collective

JULY 1, 2023

The resulting structured data is then used to train a machine learning algorithm. There are a lot of image annotation techniques that can make the process more efficient with deep learning. Provide examples and decision trees to guide annotators through complex scenarios.

Cross Validation 64

Cross Validation AI AI Machine Learning 64

DrivenData Labs

MAY 24, 2023

Her primary interests lie in theoretical machine learning. She currently does research involving interpretability methods for biological deep learning models. We chose to compete in this challenge primarily to gain experience in the implementation of machine learning algorithms for data science.

Machine Learning 225

Machine Learning Machine Learning Decision Trees Data Science 225

Pickl AI

MAY 24, 2023

List of Python Libraries and Their Uses Given below are the Python Libraries that can be identified to be important working Python Libraries used by programmers in the industry: TensorFlow It is a computational library useful for writing new algorithms involving large number of tensor operations.

Data Science 52

Data Science Python Machine Learning Machine Learning 52

Pickl AI

SEPTEMBER 3, 2023

Anomaly detection Machine Learning example: Given below are the Machine Learning anomaly detection examples that you need to know about: Network Intrusion Detection: Anomaly detection Machine Learning algorithms is used to monitor network traffic and identify unusual patterns that might indicate a cyberattack or unauthorised access.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Algorithm 52

Heartbeat

APRIL 17, 2023

Photo by Shahadat Rahman on Unsplash Introduction Machine learning (ML) focuses on developing algorithms and models that can learn from data and make predictions or decisions. Human brains are capable of processing vast amounts of information from the environment and making complex decisions based on that information.

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The MLOps Blog

DECEMBER 19, 2022

Traditional Machine Learning and Deep Learning methods are used to solve Multiclass Classification problems, but the model’s complexity increases as the number of classes increases. Particularly in Deep Learning, the network size increases as the number of classes increases. Let’s take a look at some of them.

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IBM Journey to AI blog

SEPTEMBER 5, 2023

Essentially, these chatbots operate like a decision tree. Rules-based chatbots Building upon the menu-based chatbot’s simple decision tree functionality, the rules-based chatbot employs conditional if/then logic to develop conversation automation flows.

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Defined.ai blog

AUGUST 10, 2023

These tasks may include pattern recognition, decision-making, and language understanding. On the other hand, ML, a subset of AI, involves algorithms that improve through experience. These algorithms learn from data, making the software more efficient and accurate in predicting outcomes without explicit programming.

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Pickl AI

APRIL 26, 2024

Then, I would explore forecasting models such as ARIMA, exponential smoothing, or machine learning algorithms like random forests or gradient boosting to predict future sales. Advanced Technical Questions Machine Learning Algorithms What is logistic regression, and when is it used?

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

Pickl AI

JULY 26, 2023

Variance in Machine Learning – Examples Variance in machine learning refers to the model’s sensitivity to changes in the training data, leading to fluctuations in predictions. K-Nearest Neighbors with Small k I n the k-nearest neighbours algorithm, choosing a small value of k can lead to high variance.

Machine Learning 52

Machine Learning Machine Learning Cross Validation Decision Trees 52

IBM Journey to AI blog

FEBRUARY 23, 2024

Machine learning (ML) and deep learning (DL) form the foundation of conversational AI development. ML algorithms understand language in the NLU subprocesses and generate human language within the NLG subprocesses.

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PyImageSearch

JANUARY 16, 2023

In this tutorial, you will learn about Gradient Boosting, the final precursor to XGBoost. Jump Right To The Downloads Section Scaling Kaggle Competitions Using XGBoost: Part 3 Gradient Boost at a Glance In the first blog post of this series, we went through basic concepts like ensemble learning and decision trees.

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