Data Science Dojo

JULY 23, 2024

ML models often require numerical input, so categorical data must be converted into a numerical format for effective processing and analysis. Here are some key points highlighting the importance of categorical data in machine learning: 1. This conversion allows models to process the data and extract valuable information.

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Mlearning.ai

MARCH 13, 2023

Photo by Andy Kelly on Unsplash Choosing a machine learning (ML) or deep learning (DL) algorithm for application is one of the major issues for artificial intelligence (AI) engineers and also data scientists. Explore algorithms: Research and explore different algorithms that are desired for your problem.

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Smart Data Collective

JUNE 7, 2019

Both of these types of learning are used by machine learning algorithms in modern task management applications. Here is an overview of the supervised learning algorithms that are frequently employed by task management tools. In this way, the degree of “success” of the algorithm can be known. Final Thoughts.

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Pickl AI

SEPTEMBER 16, 2024

Summary: This blog highlights ten crucial Machine Learning algorithms to know in 2024, including linear regression, decision trees, and reinforcement learning. Each algorithm is explained with its applications, strengths, and weaknesses, providing valuable insights for practitioners and enthusiasts in the field.

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Mlearning.ai

APRIL 13, 2023

Photo by Robo Wunderkind on Unsplash In general , a data scientist should have a basic understanding of the following concepts related to kernels in machine learning: 1. Support Vector Machine Support Vector Machine ( SVM ) is a supervised learning algorithm used for classification and regression analysis.

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ODSC - Open Data Science

JANUARY 7, 2025

Heres what we noticed from analyzing this data, highlighting whats remained the same over the years, and what additions help make the modern data scientist in2025. Data Science Of course, a data scientist should know data science! Joking aside, this does infer particular skills.

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IBM Journey to AI blog

DECEMBER 20, 2023

Each type and sub-type of ML algorithm has unique benefits and capabilities that teams can leverage for different tasks. What is machine learning? Instead of using explicit instructions for performance optimization, ML models rely on algorithms and statistical models that deploy tasks based on data patterns and inferences.

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Towards AI

JULY 20, 2023

This ongoing process straddles the intersection between evidence-based medicine, data science, and artificial intelligence (AI). As the capabilities of high-powered computers and ML algorithms have grown, so have opportunities to improve the SLR process.

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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. Regression modeling is a statistical tool used to find the relationship between labeled data and variable data.

Machine Learning 82

Machine Learning Machine Learning Supervised Learning K-nearest Neighbors 82

Pickl AI

JANUARY 29, 2024

Summary: In the tech landscape of 2024, the distinctions between Data Science and Machine Learning are pivotal. Data Science extracts insights, while Machine Learning focuses on self-learning algorithms. The collective strength of both forms the groundwork for AI and Data Science, propelling innovation.

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Heartbeat

OCTOBER 23, 2023

Revolutionizing Healthcare through Data Science and Machine Learning Image by Cai Fang on Unsplash Introduction In the digital transformation era, healthcare is experiencing a paradigm shift driven by integrating data science, machine learning, and information technology.

Machine Learning 52

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Heartbeat

MARCH 8, 2023

Data mining, text classification, and information retrieval are just a few applications. To extract themes from a corpus of text data and then use these themes as features in text classification algorithms, topic modeling can be used in text classification. Naive Bayes is commonly used for spam classification.

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How to Learn Machine Learning

DECEMBER 15, 2024

In the rapidly evolving world of technology, machine learning has become an essential skill for aspiring data scientists, software engineers, and tech professionals. Coursera Machine Learning Courses are an exceptional array of courses that can transform your career and technical expertise.

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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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Heartbeat

AUGUST 1, 2023

Scale-Invariant Feature Transform (SIFT) This is an algorithm created by David Lowe in 1999. It’s a general algorithm that is known as a feature descriptor. After picking the set of images you desire to use, the algorithm will detect the keypoints of the images and store them in a database. It detects corners.

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Pickl AI

MAY 10, 2023

Machine Learning is a subset of Artificial Intelligence and Computer Science that makes use of data and algorithms to imitate human learning and improving accuracy. Being an important component of Data Science, the use of statistical methods are crucial in training algorithms in order to make classification.

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Heartbeat

OCTOBER 25, 2023

Empowering Data Scientists and Machine Learning Engineers in Advancing Biological Research Image from European Bioinformatics Institute Introduction: In biological research, the fusion of biology, computer science, and statistics has given birth to an exciting field called bioinformatics.

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Pickl AI

AUGUST 1, 2023

Text Vectorization Techniques Text vectorization is a crucial step in text mining, where text data is transformed into numerical representations that can be processed by Machine Learning algorithms. Sentiment analysis techniques range from rule-based approaches to more advanced machine learning algorithms.

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Pickl AI

FEBRUARY 12, 2024

Data Science interviews are pivotal moments in the career trajectory of any aspiring data scientist. Having the knowledge about the data science interview questions will help you crack the interview. Differentiate between supervised and unsupervised learning algorithms.

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Pickl AI

NOVEMBER 20, 2024

Introduction Linear Algebra is a fundamental mathematical discipline that underpins many algorithms and techniques in Machine Learning. By understanding Linear Algebra operations, practitioners can better grasp how Machine Learning models work, optimize their performance, and implement various algorithms effectively.

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Pickl AI

MAY 31, 2023

Explore Machine Learning with Python: Become familiar with prominent Python artificial intelligence libraries such as sci-kit-learn and TensorFlow. Begin by employing algorithms for supervised learning such as linear regression , logistic regression, decision trees, and support vector machines.

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How to Learn Machine Learning

JUNE 9, 2024

This technology streamlines the model-building process while simultaneously increasing productivity by determining the best algorithms for specific data sets. Moreover, it enhances the productivity of data scientists. It dramatically shortens computing times for complex algorithms.

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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. One of the goals of ML is to enable computers to process and analyze data in a way that is similar to how humans process information.

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Pickl AI

NOVEMBER 28, 2024

Summary: The blog discusses essential skills for Machine Learning Engineer, emphasising the importance of programming, mathematics, and algorithm knowledge. Understanding Machine Learning algorithms and effective data handling are also critical for success in the field.

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AWS Machine Learning Blog

JANUARY 17, 2024

Solution overview As mentioned earlier, the AWS services that you can use for analysis of mobility data are Amazon S3, Amazon Macie, AWS Glue, S3 Object Lambda, Amazon Comprehend, and Amazon SageMaker geospatial capabilities. Data scientists can accomplish this process by connecting through Amazon SageMaker notebooks.

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Pickl AI

DECEMBER 9, 2024

Summary: Inductive bias in Machine Learning refers to the assumptions guiding models in generalising from limited data. By managing inductive bias effectively, data scientists can improve predictions, ensuring models are robust and well-suited for real-world applications.

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Pickl AI

SEPTEMBER 30, 2024

Key concepts in ML are: Algorithms : Algorithms are the mathematical instructions that guide the learning process. They process data, identify patterns, and adjust the model accordingly. Common algorithms include decision trees, neural networks, and support vector machines.

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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.

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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. There are three main types of Machine Learning: supervised learning, unsupervised learning, and reinforcement learning.

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Pickl AI

NOVEMBER 18, 2024

Key Takeaways Machine Learning Models are vital for modern technology applications. Key steps involve problem definition, data preparation, and algorithm selection. Data quality significantly impacts model performance. Ethical considerations are crucial in developing fair Machine Learning solutions.

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IBM Journey to AI blog

AUGUST 28, 2023

As it pertains to social media data, text mining algorithms (and by extension, text analysis) allow businesses to extract, analyze and interpret linguistic data from comments, posts, customer reviews and other text on social media platforms and leverage those data sources to improve products, services and processes.

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Heartbeat

OCTOBER 11, 2023

A key component of artificial intelligence is training algorithms to make predictions or judgments based on data. This process is known as machine learning or deep learning. Two of the most well-known subfields of AI are machine learning and deep learning. What is Machine Learning?

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ODSC - Open Data Science

JANUARY 5, 2024

Scikit-learn A machine learning powerhouse, Scikit-learn provides a vast collection of algorithms and tools, making it a go-to library for many data scientists. It is easy to use, with a well-documented API and a wide range of tutorials and examples available.

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Python K-nearest Neighbors Data Science Data Visualization 52

Heartbeat

MAY 22, 2023

Genetic algorithms [ 1 ] are one way to detect faces in a digital image, followed by the Eigenface technique to verify the fitness of the region of interest. We’re committed to supporting and inspiring developers and engineers from all walks of life. We pay our contributors, and we don’t sell ads.

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Pickl AI

AUGUST 9, 2024

Statistical Analysis Introducing statistical methods and techniques for analysing data, including hypothesis testing, regression analysis, and descriptive statistics. Students should gain a foundational understanding of statistics as it applies to data analytics. Students should learn how to apply machine learning models to Big Data.

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DagsHub

APRIL 21, 2024

Although MLOps is an abbreviation for ML and operations, don’t let it confuse you as it can allow collaborations among data scientists, DevOps engineers, and IT teams. Autonomous Vehicles: Automotive companies are using ML models for autonomous driving systems including object detection, path planning, and decision-making algorithms.

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Heartbeat

MARCH 21, 2023

I was interested to see what types of problems were solved and which particular algorithms were used with the different loss functions. I decided that aggregating this data would give me a rough idea about what loss functions were commonly being used to solve the different problems. These are two separate lists).

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