Data Science Dojo

JULY 2, 2024

It is a form of AI that learns, adapts, and improves as it encounters changes, both in data and the environment. Unlike traditional AI, which follows set rules and algorithms and tends to fall apart when faced with obstacles, adaptive AI systems can modify their behavior based on their experiences. What is Adaptive AI?

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Dataconomy

JULY 30, 2024

In simple terms, machine unlearning is the process of making a machine learning model forget specific data it has previously learned. This becomes crucial when the data includes sensitive private information or copyrighted material that should not have been included in the training set initially.

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Pickl AI

JUNE 2, 2023

One of the most popular algorithms in Machine Learning are the Decision Trees that are useful in regression and classification tasks. Decision trees are easy to understand, and implement therefore, making them ideal for beginners who want to explore the field of Machine Learning. What is Decision Tree in Machine Learning?

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Dataconomy

MAY 29, 2023

In their quest for effectiveness and well-informed decision-making, businesses continually search for new ways to collect information. QR codes can contain a huge amount of information, such as text, URLs, contact details, and more. In the realm of AI and ML, QR codes find diverse applications across various domains.

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Towards AI

JANUARY 11, 2024

In this article, I’ve covered one of the most famous classification and regression algorithms in machine learning, namely the Decision Tree. In contrast, Unsupervised Learning occurs when we lack prior knowledge of the target variable. This often occurs in Cluster Analysis, where we identify clusters without prior information.

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Dataconomy

APRIL 18, 2023

Additionally, it is crucial to comprehend the fundamental concepts that underlie AI, including neural networks, algorithms, and data structures. AI systems use a combination of algorithms, machine learning techniques, and data analytics to simulate human intelligence. What is artificial intelligence?

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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.

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Data Science Dojo

AUGUST 19, 2024

They dive deep into artificial neural networks, algorithms, and data structures, creating groundbreaking solutions for complex issues. These professionals venture into new frontiers like machine learning, natural language processing, and computer vision, continually pushing the limits of AI’s potential.

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Dataconomy

SEPTEMBER 13, 2023

Imagine a world where computers can’t interpret the visual information around them without a little human assistance. These labels provide crucial context for machine learning models, enabling them to make informed decisions and predictions. That’s where data annotation comes into play.

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Dataconomy

MAY 4, 2023

From predicting disease outbreaks to identifying complex medical patterns and helping researchers develop targeted therapies, the potential applications of machine learning in healthcare are vast and varied. What is machine learning? From personalized medicine to disease prevention, the possibilities are endless.

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How to Learn Machine Learning

OCTOBER 10, 2024

The quality of your training data in Machine Learning (ML) can make or break your entire project. Machine learning algorithms rely heavily on the data they are trained on. The process involves feeding the model vast amounts of data, allowing it to recognize patterns, classify information, and make predictions.

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BAIR

APRIL 20, 2022

A demonstration of the RvS policy we learn with just supervised learning and a depth-two MLP. It uses no TD learning, advantage reweighting, or Transformers! Offline reinforcement learning (RL) is conventionally approached using value-based methods based on temporal difference (TD) learning.

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Pickl AI

SEPTEMBER 3, 2024

Introduction Data annotation is the process of adding meaningful labels, tags, or metadata to raw data to provide context and structure for Machine Learning algorithms. It lays the groundwork for training models, ensuring accuracy, and facilitating supervised learning.

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Mlearning.ai

FEBRUARY 17, 2023

Basically, Machine learning is a part of the Artificial intelligence field, which is mainly defined as a technic that gives the possibility to predict the future based on a massive amount of past known or unknown data. ML algorithms can be broadly divided into supervised learning , unsupervised learning , and reinforcement learning.

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Dataconomy

MARCH 3, 2023

It’s really important to explore the potential of machine learning in asset pricing. In the fast-paced world of finance, accurate and timely asset pricing is essential for making informed investment decisions. As a result, financial analysts have turned to machine learning as a promising tool for improving asset pricing models.

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Data Science Dojo

SEPTEMBER 11, 2023

These complex algorithms are the backbone upon which our modern technological advancements rest and which are doing wonders for natural language communication. PaLM 2 stands for “ Progressive and Adaptive Language Model 2 ” and Llama 2 is short for “ Language Learning and Mastery Algorithm 2 ”.

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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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Towards AI

JULY 22, 2023

We’re educating the computer to learn from data (the equivalent of practice), to make informed predictions (akin to riding the bicycle), and to progressively improve with each iteration. In the context of Machine Learning, data can be anything from images, text, numbers, to anything else that the computer can process and learn from.

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Dataconomy

OCTOBER 5, 2023

Data preprocessing tasks can include data cleaning to remove errors or inconsistencies, normalization to bring data within a consistent range, and feature engineering to extract meaningful information from raw data. AI practitioners choose an appropriate machine learning model or algorithm that aligns with the problem at hand.

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Google Research AI blog

MARCH 6, 2023

First, there is a lack of scalability with conventional supervised learning approaches. In contrast, self-supervised learning can leverage audio-only data, which is available in much larger quantities across languages. This requires the learning algorithm to be flexible, efficient, and generalizable.

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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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Pickl AI

AUGUST 6, 2023

Machine Learning has become a fundamental part of people’s lives and it typically holds two segments. It includes supervised and unsupervised learning. Supervised Learning deals with labels data and unsupervised learning deals with unlabelled data. What is Regression in ML? You can join Pickl.AI

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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? Supervised Learning Models Supervised learning involves training a model on labelled data, where the input features and corresponding target outputs are provided.

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Data Science Dojo

JULY 4, 2024

Subjectivity Handling : Since human feedback can capture nuances and subjective assessments that are challenging to define algorithmically, RLHF is particularly effective for tasks that require a deep understanding of context and user intent. PPO is favored for its relative simplicity and effectiveness in handling large-scale models.

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Mlearning.ai

JUNE 27, 2023

Images and videos may have a wealth of information, such as various items, backdrops, lighting conditions, and viewpoints. These variances provide a significant level of uncertainty and ambiguity, necessitating the use of computer vision algorithms to effectively and reliably process and understand this large array of data.

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AWS Machine Learning Blog

DECEMBER 7, 2023

Amazon Forecast is a fully managed service that uses machine learning (ML) algorithms to deliver highly accurate time series forecasts. Calculating courier requirements The first step is to estimate hourly demand for each warehouse, as explained in the Algorithm selection section.

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NYU Center for Data Science

SEPTEMBER 13, 2024

The world of multi-view self-supervised learning (SSL) can be loosely grouped into four families of methods: contrastive learning, clustering, distillation/momentum, and redundancy reduction. This work not only brings new theoretical insights but also introduces practical tools to optimize self-supervised models.

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ODSC - Open Data Science

SEPTEMBER 15, 2023

With its advanced algorithms and language comprehension, it can navigate complex datasets and distill valuable insights. Imagine equipping generative AI with a dataset rich in information from various sources. This synthetic data serves as a viable alternative for training models, testing algorithms, and ensuring privacy compliance.

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Dataconomy

APRIL 3, 2023

ML models are designed to learn from data and make predictions or decisions based on that data. Types of ML There are three main types of machine learning: Supervised learning: In supervised learning, the algorithm is trained on labeled data.

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Dataconomy

APRIL 3, 2023

ML models are designed to learn from data and make predictions or decisions based on that data. Types of ML There are three main types of machine learning: Supervised learning: In supervised learning, the algorithm is trained on labeled data.

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Data Science Dojo

MAY 10, 2024

Stage 2: Introduction of neural networks The next step for LLM embeddings was the introduction of neural networks to capture the contextual information within the data. SOMs work to bring down the information into a 2-dimensional map where similar data points form clusters, providing a starting point for advanced embeddings.

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Mlearning.ai

NOVEMBER 23, 2023

This Only Applies to Supervised Learning Introduction If you’re like me then you probably like a more intuitive way of doing things. When it comes to machine learning, we often have that one (or two or three) “go-to” model(s) that we tend to rely on for most problems. To see the first 5 predictions of each model.

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Pickl AI

AUGUST 14, 2024

History and Evolution of Neural Networks The concept of neural networks dates back to the 1940s, with the introduction of the perceptron by Frank Rosenblatt, which laid the groundwork for supervised learning. Optimization Algorithms Optimization algorithms play a crucial role in training neural networks.

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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 Dojo

SEPTEMBER 1, 2023

Understanding fine-tuning, even if not doing it yourself, aids in informed decision-making. This allows them to learn a wide range of tasks, such as text generation, translation, and question-answering. One common approach is to use supervised learning. This process involves retraining LLMs on new data.

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ODSC - Open Data Science

MARCH 22, 2023

Cleanlab is an open-source software library that helps make this process more efficient (via novel algorithms that automatically detect certain issues in data) and systematic (with better coverage to detect different types of issues). Data-centric AI instead asks how we can systematically engineer better data through algorithms/automation.

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Pickl AI

AUGUST 1, 2023

Text mining is primarily a technique in the field of Data Science that encompasses the extraction of meaningful insights and information from unstructured textual data. This helps businesses gain insights into market trends, consumer preferences, and competitive landscapes, allowing them to make informed strategic decisions.

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