Data Science Dojo

JUNE 1, 2023

The course covers topics such as linear regression, logistic regression, and decision trees. Machine Learning for Natural Language Processing by Christopher Manning, Jurafsky and Schütze This is an advanced-level course that teaches you how to use machine learning for natural language processing tasks.

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Heartbeat

FEBRUARY 8, 2023

Source: Author The field of natural language processing (NLP), which studies how computer science and human communication interact, is rapidly growing. By enabling robots to comprehend, interpret, and produce natural language, NLP opens up a world of research and application possibilities.

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Data Science Dojo

AUGUST 27, 2024

Algorithms: Decision trees, random forests, logistic regression, and more are like different techniques a detective might use to solve a case. Algorithms: Decision trees, random forests, logistic regression, and more are like different techniques a detective might use to solve a case.

Data Scientist 195

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Heartbeat

JULY 18, 2023

Source: Author Natural Language Processing (NLP) is a field of study focused on allowing computers to understand and process human language. There are many different NLP techniques and tools available, including the R programming language. We will be using the “tm” package for preprocessing.

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Data Science Dojo

AUGUST 30, 2023

It directly focuses on implementing scientific methods and algorithms to solve real-world business problems and is a key player in transforming raw data into significant and actionable business insights. Machine learning algorithms Machine learning forms the core of Applied Data Science.

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Towards AI

SEPTEMBER 5, 2024

Featured Community post from the Discord Aman_kumawat_41063 has created a GitHub repository for applying some basic ML algorithms. It offers pure NumPy implementations of fundamental machine learning algorithms for classification, clustering, preprocessing, and regression. Learn AI Together Community section!

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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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Dataconomy

JULY 18, 2023

By leveraging artificial intelligence algorithms and data analytics, manufacturers can streamline their quoting process, improve accuracy, and gain a competitive edge in the market. This information helps businesses estimate the resources required and adjust pricing accordingly in real-time.

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Pickl AI

SEPTEMBER 7, 2023

When you start exploring more about Machine Learning, you will come across the Gradient Boosting Algorithm. Basically, it is a powerful and versatile machine-learning algorithm that falls under the category of ensemble learning. Machine Learning models can leave you spellbound by their efficiency and proficiency.

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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. 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. What is machine learning?

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Towards AI

FEBRUARY 20, 2024

Beginner’s Guide to ML-001: Introducing the Wonderful World of Machine Learning: An Introduction Everyone is using mobile or web applications which are based on one or other machine learning algorithms. You might be using machine learning algorithms from everything you see on OTT or everything you shop online.

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Dataconomy

MAY 25, 2023

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. This enables them to extract valuable insights, identify patterns, and make informed decisions in real-time.

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Dataconomy

AUGUST 31, 2023

Natural Language Processing (NLP) : Classification can be applied to text data to categorize messages, emails, or social media posts into different categories, such as spam vs. non-spam, positive vs. negative sentiment, or topic classification. Next, you need to select a model.

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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. However, the process of creating AI can seem daunting to those who are unfamiliar with the technicalities involved. Train and evaluate the AI models for accuracy and efficiency.

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Dataconomy

JUNE 13, 2023

Decision intelligence is an innovative approach that blends the realms of data analysis, artificial intelligence, and human judgment to empower businesses with actionable insights. Think of decision intelligence as a synergy between the human mind and cutting-edge algorithms. What is decision intelligence?

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Pickl AI

JANUARY 2, 2025

Summary: Entropy in Machine Learning quantifies uncertainty, driving better decision-making in algorithms. It optimises decision trees, probabilistic models, clustering, and reinforcement learning. Key Takeaways Entropy measures randomness, guiding algorithms to make better decisions.

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Pickl AI

DECEMBER 9, 2024

Types of inductive bias include prior knowledge, algorithmic bias, and data bias. Inductive bias helps in this process by limiting the search space, making it computationally feasible to find a good solution. In contrast, decision trees assume data can be split into homogeneous groups through feature thresholds.

Machine Learning 52

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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. What is Classification?

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Pickl AI

JULY 12, 2024

Their interactive nature makes them suitable for experimenting with AI algorithms and analysing data. Machine Learning algorithms are trained on large amounts of data, and they can then use that data to make predictions or decisions about new data.

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IBM Journey to AI blog

JUNE 5, 2024

Modern SaaS analytics solutions can seamlessly integrate with AI models to predict user behavior and automate data sorting and analysis; and ML algorithms enable SaaS apps to learn and improve over time. AI and ML algorithms enhance these features by processing unique app data more efficiently. Predictive analytics.

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Pickl AI

AUGUST 1, 2023

Machine Learning models play a crucial role in this process, serving as the backbone for various applications, from image recognition to natural language processing. Examples of supervised learning models include linear regression, decision trees, support vector machines, and neural networks.

Machine Learning 52

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Dataconomy

APRIL 4, 2023

Artificial Intelligence (AI) models are the building blocks of modern machine learning algorithms that enable machines to learn and perform complex tasks. These models are designed to replicate the human brain’s cognitive functions, enabling them to perceive, reason, learn, and make decisions based on data. What is an AI model?

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Dataconomy

APRIL 4, 2023

Artificial Intelligence (AI) models are the building blocks of modern machine learning algorithms that enable machines to learn and perform complex tasks. These models are designed to replicate the human brain’s cognitive functions, enabling them to perceive, reason, learn, and make decisions based on data. What is an AI model?

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Pickl AI

APRIL 9, 2025

Language Understanding: Processing and interpreting human language (Natural Language Processing – NLP). A simple example could be an early chess-playing program that evaluated moves based on predefined rules and search algorithms. Planning: Devising sequences of actions to achieve goals.

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Pickl AI

DECEMBER 4, 2024

These base learners may vary in complexity, ranging from simple decision trees to complex neural networks. decision trees) is trained on each subset. Examples Random Forest, which builds an ensemble of decision trees. Works well with unstable models like decision trees. A base model (e.g.,

Machine Learning 52

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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. In both cases, algorithms are trained to generate predictions or judgments based on data inputs. What is Machine Learning?

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Pickl AI

JANUARY 29, 2024

Data Science extracts insights, while Machine Learning focuses on self-learning algorithms. Key takeaways Data Science lays the groundwork for Machine Learning, providing curated datasets for ML algorithms to learn and make predictions. AI comprises Natural Language Processing, computer vision, and robotics.

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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.

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Pickl AI

NOVEMBER 18, 2024

Key steps involve problem definition, data preparation, and algorithm selection. Basics of Machine Learning Machine Learning is a subset of Artificial Intelligence (AI) that allows systems to learn from data, improve from experience, and make predictions or decisions without being explicitly programmed.

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Pickl AI

FEBRUARY 7, 2025

ML algorithms use statistical methods to identify patterns in data, allowing systems to make predictions or decisions without human intervention. Over time, these models refine their accuracy as they process more data, which enables continuous improvement and adaptation. billion by 2034.

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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. synonyms).

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Pickl AI

OCTOBER 18, 2023

Algorithms for Data Quality Enhancement Choosing the right algorithms and queries is imperative for companies dealing with extensive datasets. Random Forest: A Versatile Machine Learning Algorithm Random Forest is a flexible and widely machine-learning algorithm known for its simplicity and reliability.

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ODSC - Open Data Science

SEPTEMBER 18, 2023

Getting started with natural language processing (NLP) is no exception, as you need to be savvy in machine learning, deep learning, language, and more. You’ll see how these models can outperform counting-based methods by better-capturing language’s semantic subtleties and complexities.

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Pickl AI

FEBRUARY 19, 2025

Algorithms like AdaBoost, XGBoost, and LightGBM power real-world finance, healthcare, and NLP applications. Despite computational costs, Boosting remains vital for handling complex data and optimising AI models for high-performance decision-making. This blog explores how Boosting works and its popular algorithms.

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IBM Journey to AI blog

JULY 6, 2023

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. It processes enormous amounts of data a human wouldn’t be able to work through in a lifetime and evolves as more data is processed.

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Mlearning.ai

FEBRUARY 15, 2023

Introduction In natural language processing, text categorization tasks are common (NLP). Figure 4 Data Cleaning Conventional algorithms are often biased towards the dominant class, ignoring the data distribution. A random forest is an ensemble classifier that makes predictions using a variety of decision trees.

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Pickl AI

OCTOBER 24, 2024

Key Takeaways Scope and Purpose : Artificial Intelligence encompasses a broad range of technologies to mimic human intelligence, while Machine Learning focuses explicitly on algorithms that enable systems to learn from data. Supervised Learning : This is the most common form of ML, where algorithms learn from labelled data.

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