Heartbeat

JANUARY 24, 2024

Making visualizations is one of the finest ways for data scientists to explain data analysis to people outside the business. Exploratory data analysis can help you comprehend your data better, which can aid in future data preprocessing. Let’s examine some charts that can be used to display categorical data.

Data Analysis 52

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NVIDIA

NOVEMBER 8, 2023

207 While an AI designed for categorizing traffic lights, for example, doesn’t need perfection, medical tools must be highly accurate — any oversight could be fatal. Currently, Annalise.ai works for chest X-rays and brain CT scans, with more on the way. The AI Podcast · Harrison.ai To overcome this challenge, annalise.ai

Categorization 76

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NVIDIA

NOVEMBER 8, 2023

207 While an AI designed for categorizing traffic lights, for example, doesn’t need perfection, medical tools must be highly accurate — any oversight could be fatal. Currently, Annalise.ai works for chest X-rays and brain CT scans, with more on the way. The AI Podcast · Harrison.ai To overcome this challenge, annalise.ai

Categorization 75

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Mlearning.ai

FEBRUARY 6, 2024

Exploratory Data Analysis(EDA)on Biological Data: A Hands-On Guide Unraveling the Structural Data of Proteins, Part II — Exploratory Data Analysis Photo from Pexels In a previous post, I covered the background of this protein structure resolution data set, including an explanation of key data terminology and details on how to acquire the data.

Data Analysis 52

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IBM Journey to AI blog

OCTOBER 16, 2023

Marketers use ML for lead generation, data analytics, online searches and search engine optimization (SEO). ML algorithms and data science are how recommendation engines at sites like Amazon, Netflix and StitchFix make recommendations based on a user’s taste, browsing and shopping cart history.

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Mlearning.ai

JULY 25, 2023

Data Science techniques are always improving. I started summarizing my projects into case studies after finishing the Google Data Analytics Professional Certificate , the specialization includes 8 courses and the last course entails completing two case studies using semi-large datasets.

Data Science 52

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Mlearning.ai

FEBRUARY 11, 2023

This parametric test assumes that the sample data comes from normally distributed populations. Also, this parametric test is not suitable for categorical variables. They can only involve a categorical variable as an independent variable. If the categorical variable is the dependent variable, t-tests are not appropriate.

Categorization 52

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Mlearning.ai

MAY 23, 2023

Hey guys, in this blog we will see some of the most asked Data Science Interview Questions by interviewers in [year]. Data science has become an integral part of many industries, and as a result, the demand for skilled data scientists is soaring. What is Data Science?

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Topbots

JUNE 22, 2023

So, to make a viable comparison, I had to: Categorize the dataset scores into Positive , Neutral , or Negative labels. This evaluation assesses how the accuracy (y-axis) changes regarding the threshold (x-axis) for categorizing the numeric Gold-Standard dataset for both models. First, I must be honest. Then, I made a confusion matrix.

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ODSC - Open Data Science

JULY 26, 2023

Data Preprocessing Data preprocessing involves cleaning and transforming raw data into a format suitable for machine learning algorithms. This step may include handling missing values, outlier detection, feature scaling, encoding categorical variables, and feature engineering.

Machine Learning 52

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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. Data Visualisation What are the fundamental principles of data visualisation?

Data Analysis 52

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NYU Center for Data Science

APRIL 17, 2023

“If we compare the relative performance of Silicon Valley’s stock to that of JP Morgan and Bank of America since 1993, its market value rose 250-fold until the market’s peak on Nov 3, 2021, relative to 11-fold for JP Morgan and three-fold for Bank of America,” Vasant explained. So how can algorithms recognize overreactions?

Algorithm 61

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Heartbeat

OCTOBER 11, 2023

Instead of memorizing the training data, the objective is to create models that precisely predict unobserved instances. Supervised, unsupervised, and reinforcement learning : Machine learning can be categorized into different types based on the learning approach.

Deep Learning 52

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Flipboard

SEPTEMBER 4, 2023

A high school English teacher recently explained to me how she’s coping with the latest challenge to education in America: ChatGPT. the Stanford scientists wrote in a July 2023 paper , published under the banner, “ opinion ,” in the peer-reviewed data science journal Patterns.

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Heartbeat

SEPTEMBER 28, 2023

Hands-on Project Why customer churn matters and how to predict it with machine learning, explained step-by-step Photo by Gabrielle Ribeiro on Unsplash Introduction In today’s competitive business environment, retaining customers is essential to a company’s success. Preprocessing Data preprocessing is a critical step.

Machine Learning 52

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Artificial Corner

JUNE 4, 2023

I will explain what these measures mean in plain english , so that you can at least grasp the intuition behind them. If our attribute is categorical (e.g., NetworkX will automatically detect wether your attribute is categorical or numerical and behave accordingly. Let’s go over the first one.

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Heartbeat

NOVEMBER 14, 2023

It has an excellent reputation as a tool for predicting many kinds of problems in data science and machine learning. Architecture This model's architecture is categorized into two sections: The feature learner and the class predictor. The feature learning section has an input section and a data preprocessing section.

Deep Learning 52

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Towards AI

FEBRUARY 3, 2024

Even though converting raw data into actionable insights, it is not determined by ML algorithms alone. In this article, I am going to explain in detail step-by-step approaches or stages of the machine learning project lifecycle. The success of any ML project depends on a well-structured lifecycle.

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Mlearning.ai

FEBRUARY 11, 2023

In our case, we make a statement that explains a relationship between two variables. An assumption that is made so as to be tested for the credibility. The assumption is made based on the default states and facts we see, but are not yet proven.

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Heartbeat

FEBRUARY 23, 2023

Image Courtesy of UrielSC A big part of data science is tuning our models and improving upon them over time. Afterwards, we’ll go through the code step-by-step and explain what each part is doing. We choose our usual loss function and metric for a multi-categorical problem. Well, here’s the guide for you. We asked them!

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Heartbeat

FEBRUARY 2, 2024

COCO-QA: Shifting attention to COCO-QA, questions are categorized based on types such as color, counting, location, and object. This categorization lays the groundwork for nuanced evaluation, recognizing that different question types demand distinct reasoning strategies from VQA algorithms. In xxAI — Beyond Explainable AI Chapter.

Computer Vision 52

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Viso.ai

APRIL 17, 2024

The type of features and extraction techniques also vary depending on the input data type. While working with tabular data, we have both numerical (e.g. Age, No of products, and categorical features (Gender, Country, etc). In deep learning models that use image data, features include detected edges, pixel data, exposure, etc.

Python 52

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Towards AI

FEBRUARY 20, 2024

If you want an overview of the Machine Learning Process, it can be categorized into 3 wide buckets: Collection of Data: Collection of Relevant data is key for building a Machine learning model. It isn't easy to collect a good amount of quality data. How Machine Learning Works? Models […]

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Heartbeat

DECEMBER 13, 2023

0.9]] logloss = log_loss(y_true, y_pred) print("Log Loss:", logloss) Cohen's Kappa Cohen's Kappa is a statistical measure of inter-rater agreement between two raters for categorical items. Example: #Log loss evaluation metric from sklearn.metrics import log_loss y_true = [0, 1, 1, 0, 1] y_pred = [[0.89, 0.11], [0.3,

Machine Learning 52

Machine Learning Data Scientist Deep Learning Categorization 52

IBM Journey to AI blog

APRIL 18, 2024

Most experts categorize it as a powerful, but narrow AI model. Self-driving cars excel at navigating roads and supercomputers like IBM Watson ® can analyze vast amounts of data. Building an in-house team with AI, deep learning , machine learning (ML) and data science skills is a strategic move.

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Heartbeat

NOVEMBER 15, 2023

We’re attempting label encoding, a technique for converting categorical variables into numerical form. Digits=LabelEncoder() The fit() method of the LabelEncoder() class takes a list of categorical values as input and assigns a unique numerical label to each distinct category.

Machine Learning 52

Machine Learning Categorization Data Scientist Deep Learning 52

Towards AI

JULY 22, 2023

What are Large Language Models and why do they need data? We’ll start with a simple explainer of how Machine Learning models work — let’s say you want to predict how late your upcoming flight’s arrival time will be. Therefore, having a massive volume of data is critical and companies that train these models need to source this data.

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Heartbeat

MARCH 1, 2023

Once trained, the model can be used to categorize new, unseen photos. Numerous applications, including object identification, image search, and image annotation, can benefit from image categorization. The image is subsequently classified using the convolutional and pooling layers’ retrieved features by the fully connected layers.

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Heartbeat

FEBRUARY 21, 2024

Categorical Cross-Entropy Loss (Softmax Loss): Used for multi-class classification problems. AdaGrad Explained | Papers With Code The optimizer updates the internal parameters of the model based on the error calculated by the loss function. All the elements we have explained, such as optimizer, loss function, and epoch, work together.

Deep Learning 52

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Towards AI

SEPTEMBER 22, 2023

Hello, Data Enthusiast? We know that data is better understood through visualizations. It doesn’t just help us get insights, Visualizations also make things easy to explain our insights to clients or others. So, We can say that learning to make better visualizations is essential for any data enthusiast!

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Heartbeat

AUGUST 21, 2023

AlexNet was created to categorize photos in the ImageNet dataset, which contains approximately 1 million images divided into 1,000 categories. Natural Language Processing : CNNs have been implemented for sentiment analysis and text categorization in natural language processing jobs.

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Heartbeat

AUGUST 23, 2023

Carefully examining and categorizing these materials can be time-consuming and laborious. On the other hand, NLP-powered algorithms can quickly process and categorize massive amounts of data, minimizing the time necessary for initial case assessment and information retrieval.

NLP 52

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Mlearning.ai

JULY 2, 2023

Following these steps, I will preprocess the data and perform a multivariate forecasting study. Pixabay, Pexels Hello, today I am thrilled to finally sit in front of my computer and delve into an exciting data science project that I have been putting off for quite some time. df.loc[(df["Postal Code"].isnull()),

Categorization 52

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Heartbeat

FEBRUARY 15, 2023

As a machine learning engineer, you start with a labeled data set that has vast amounts of text that have already been categorized. Editor’s Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners.

ML 52

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Mlearning.ai

JANUARY 26, 2023

When it comes to implementing any ML model, the most difficult question asked is how do you explain it. Suppose, you are a data scientist working closely with stakeholders or customers, even explaining the model performance and feature selection of a Deep learning model is quite a task. How can we explain it in simple terms?

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AWS Machine Learning Blog

AUGUST 4, 2023

Use a custom transform step to create categorical values for state__c , case_count__c , and tenure features. Use the Handle missing step with the Drop missing transform to drop rows with missing values for various features. We apply this transformation on all columns. otherwise( when(df.case_count__c <= 2, “1 to 2 Cases”).otherwise(“Greater

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Heartbeat

JUNE 28, 2023

Types of Knowledge Distillation It is broadly categorized into two main categories:- Soft target distillation: In this type of knowledge distillation, the teacher model generates soft targets in the form of probability distributions for each input example, which are then used to train the student model.

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