Mlearning.ai

FEBRUARY 2, 2023

Data scientists use a technique called cross validation to help estimate the performance of a model as well as prevent the model from… Continue reading on MLearning.ai »

Cross Validation 52

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Data Science Dojo

JULY 5, 2024

Scikit-learn Scikit-learn is a versatile Python library that offers various algorithms and model evaluation metrics, including cross-validation and grid search for hyperparameter tuning. It is widely used for data mining, analysis, and machine learning tasks.

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Data Science Dojo

AUGUST 19, 2024

Industry Adoption: Widespread Implementation: AI and data science are being adopted across various industries, including healthcare, finance, retail, and manufacturing, driving increased demand for skilled professionals. This happens when the model is too simple to capture the underlying patterns in the data.

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Pickl AI

MAY 24, 2023

Introduction One of the most widely used and highly popular programming languages in the technological world is Python. Significantly, despite being user-friendly and easy to learn, one of Python’s many advantages is that it has large collection of libraries. What is a Python Library? What version of Python are you using?

Data Science 52

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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. Data Science skills that will help you excel professionally.

Data Science 52

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Pickl AI

SEPTEMBER 12, 2024

Summary : This article equips Data Analysts with a solid foundation of key Data Science terms, from A to Z. Introduction In the rapidly evolving field of Data Science, understanding key terminology is crucial for Data Analysts to communicate effectively, collaborate effectively, and drive data-driven projects.

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Dataconomy

JUNE 29, 2023

Python machine learning packages have emerged as the go-to choice for implementing and working with machine learning algorithms. These libraries, with their rich functionalities and comprehensive toolsets, have become the backbone of data science and machine learning practices. Why do you need Python machine learning packages?

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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?

Data Science 52

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Data Science Dojo

AUGUST 24, 2023

When the ML lifecycle is not properly streamlined with MLOps, organizations face issues such as inconsistent results due to varying data quality, slower deployment as manual processes become bottlenecks, and difficulty maintaining and updating models rapidly enough to react to changing business conditions.

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Mlearning.ai

FEBRUARY 15, 2024

Data Science Project — Predictive Modeling on Biological Data Part III — A step-by-step guide on how to design a ML modeling pipeline with scikit-learn Functions. Photo by Unsplash Earlier we saw how to collect the data and how to perform exploratory data analysis. You can refer part-I and part-II of this article.

Data Science 52

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Heartbeat

FEBRUARY 20, 2023

First-time project and model registration Photo by Isaac Smith on Unsplash The world of machine learning and data science is awash with technicalities. Model Extraction and Registration For the first version, I want to fit a KNeighborsClassifier to fit the data.

Cross Validation 87

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Pickl AI

NOVEMBER 2, 2023

It serves as a handy quick-reference tool to assist data professionals in their work, aiding in data interpretation, modeling , and decision-making processes. In the fast-paced world of Data Science, having quick and easy access to essential information is invaluable when using a repository of Cheat sheets for Data Scientists.

Data Scientist 52

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Mlearning.ai

SEPTEMBER 24, 2023

Figure 1: Brute Force Search It is a cross-validation technique. It trains several models using k — 1 of the folds as training data. The remaining fold is used as test data to compute a performance measure. Figure 2: K-fold Cross Validation On the one hand, it is quite simple. 2019) Data Science with Python.

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Mlearning.ai

MARCH 15, 2023

Build a Stocks Price Prediction App powered by Snowflake, AWS, Python and Streamlit — Part 2 of 3 A comprehensive guide to develop machine learning applications from start to finish. Introduction Welcome Back, Let's continue with our Data Science journey to create the Stock Price Prediction web application.

Python 52

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Pickl AI

FEBRUARY 22, 2024

Summary: Dive into programs at Duke University, MIT, and more, covering Data Analysis, Statistical quality control, and integrating Statistics with Data Science for diverse career paths. offer modules in Statistical modelling, biostatistics, and comprehensive Data Science bootcamps, ensuring practical skills and job placement.

Hypothesis Testing 52

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Ocean Protocol

FEBRUARY 1, 2024

This is a unique opportunity for data people to dive into real-world data and uncover insights that could shape the future of aviation safety, understanding, airline efficiency, and pilots driving planes. When implementing these models, you’ll typically start by preprocessing your time series data (e.g.,

Exploratory Data Analysis 52

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Pickl AI

NOVEMBER 28, 2024

Key programming languages include Python and R, while mathematical concepts like linear algebra and calculus are crucial for model optimisation. Understanding Machine Learning algorithms and effective data handling are also critical for success in the field. This growth signifies Python’s increasing role in ML and related fields.

Machine Learning 52

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Pickl AI

MARCH 26, 2024

Experimentation and cross-validation help determine the dataset’s optimal ‘K’ value. Distance Metrics Distance metrics measure the similarity between data points in a dataset. Implementing the KNN algorithm involves several steps, from preprocessing the data to training the model and making predictions.

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AWS Machine Learning Blog

DECEMBER 13, 2024

This allows scientists and model developers to focus on model development and rapid experimentation rather than infrastructure management Pipelines offers the ability to orchestrate complex ML workflows with a simple Python SDK with the ability to visualize those workflows through SageMaker Studio. tag = "latest" container_image_uri = "{0}.dkr.ecr.{1}.amazonaws.com/{2}:{3}".format(account_id,

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Mlearning.ai

NOVEMBER 30, 2023

Decision Trees Decision trees in machine learning are like flowcharts, making decisions based on data. For our Python example, we’ll use the famous Iris dataset from scikit-learn, which includes measurements of iris flowers and their species. They’re particularly useful in scenarios requiring clear, logical decisions.

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Mlearning.ai

JUNE 28, 2023

{This article was written without the assistance or use of AI tools, providing an authentic and insightful exploration of PyCaret} Image by Author ‍In the rapidly evolving realm of data science, the imperative to automate machine learning workflows has become an indispensable requisite for enterprises aiming to outpace their competitors.

Machine Learning 52

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Pickl AI

DECEMBER 12, 2024

These features collectively make XGBoost a robust, high-performance tool for modern Data Science challenges. The algorithms ability to uncover intricate patterns and relationships ensures its popularity in Data Science challenges. Each algorithm has strengths and weaknesses, making them suitable for different scenarios.

Machine Learning 52

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Pickl AI

SEPTEMBER 10, 2023

You can use techniques like grid search, cross-validation, or optimization algorithms to find the best parameter values that minimize the forecast error. It’s important to consider the specific characteristics of your data and the goals of your forecasting project when configuring the model.

Data Analyst 52

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Mlearning.ai

FEBRUARY 10, 2023

For example, if you are using regularization such as L2 regularization or dropout with your deep learning model that performs well on your hold-out-cross-validation set, then increasing the model size won’t hurt performance, it will stay the same or improve. The only drawback of using a bigger model is computational cost.

Machine Learning 52

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Pickl AI

OCTOBER 28, 2024

Example of Bootstrap Method (With Code) The Bootstrap Method is a powerful statistical technique that allows you to estimate the distribution of a statistic by resampling your data with replacement. This example illustrates how to implement the bootstrap method in Python effectively.

Hypothesis Testing 52

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Pickl AI

APRIL 26, 2024

Overfitting occurs when a model learns the training data too well, including noise and irrelevant patterns, leading to poor performance on unseen data. Techniques such as cross-validation, regularisation , and feature selection can prevent overfitting. How do you handle large datasets in Python?

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Heartbeat

DECEMBER 19, 2023

Integration of Comet: Unleashing the Power of Experiment Management and Performance Tracking We need to install and set Comet up to get started with the library in our Python environment. Each user has assigned a rating from 1 to 5 to different items based on their preferences. Happy exploring and optimizing!

Deep Learning 52

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phData

AUGUST 1, 2023

Dataiku is an industry-leading Data Science and Machine Learning platform that allows business and technical experts to work together in a shared environment. In this blog, we will cover what plugins are, why they are useful, and an example of how to develop one using the NeuralProphet Python package and Snowflake Data Cloud.

Python 40

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phData

AUGUST 1, 2023

Dataiku is an industry-leading Data Science and Machine Learning platform that allows business and technical experts to work together in a shared environment. In this blog, we will cover what plugins are, why they are useful, and an example of how to develop one using the NeuralProphet Python package and Snowflake Data Cloud.

Python 40

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Pickl AI

SEPTEMBER 18, 2024

Most professionals in this field start with a bachelor’s degree in computer science, Data Science, mathematics, or a related discipline. These programs provide the fundamental knowledge to understand complex algorithms, data structures, and statistical methods. Pursuing a master’s or even a Ph.D.

Machine Learning 52

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Pickl AI

AUGUST 9, 2024

Apache Spark A fast, in-memory data processing engine that provides support for various programming languages, including Python, Java, and Scala. Model Evaluation Techniques for evaluating machine learning models, including cross-validation, confusion matrix, and performance metrics.

Big Data 52

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Mlearning.ai

APRIL 13, 2023

This is often done using techniques such as cross-validation or grid search. Submission Suggestions What a data scientist should know about machine learning kernels? Submission Suggestions What a data scientist should know about machine learning kernels? It is particularly useful for datasets with complex patterns.

Machine Learning 52

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Heartbeat

MAY 29, 2023

Transfer learning uses knowledge acquired from previous training and applies it to a new task; image from data-science-blog.com Transfer learning can also help to mitigate the problem of data sparsity, where the model is trained on a small number of examples that may not be representative of the true distribution of the data.

Machine Learning 78

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Mlearning.ai

JANUARY 31, 2023

In particular, my code is based on rospy, which, as you might guess, is a python package allowing you to write code to interact with ROS. There, you will find a quick notebook on which you can test the performance of an SVM on the data annotated with both the labels “by hand” and the labels provided by the K-means. References [1] I.

Clustering 52

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Pickl AI

OCTOBER 15, 2024

Splitting your data into training and test sets is essential to ensure the model doesn’t memorise the data but instead learns generalisable patterns. Model Validation Model validation is a critical step to evaluate the model’s performance on unseen data.

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The MLOps Blog

DECEMBER 7, 2022

Since they were mostly doing PoCs for testing, most of the architecture was compute-oriented : setting things up in EC2 via ssh, no CI/CD, almost unused RDSs, some problems with missing data, and close to no containerization at all, etc. With all of that, the model gets retrained with all the data and stored in the Sagemaker Model Registry.

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AWS Machine Learning Blog

OCTOBER 3, 2024

Users without data science or analytics experience can generate rigorous data-backed predictions to answer big questions like time-to-fill for important positions, or resignation risk for crucial employees. The data science team couldn’t roll out changes independently to production.

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