Mlearning.ai

NOVEMBER 30, 2023

Mastering Tree-Based Models in Machine Learning: A Practical Guide to Decision Trees, Random Forests, and GBMs Image created by the author on Canva Ever wondered how machines make complex decisions? Just like a tree branches out, tree-based models in machine learning do something similar.

Machine Learning 52

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DrivenData Labs

JANUARY 22, 2025

Final Stage Overall Prizes where models were rigorously evaluated with cross-validation and model reports were judged by a panel of experts. The cross-validations for all winners were reproduced by the DrivenData team. Lower is better. Unsurprisingly, the 0.10 quantile was easier to predict than the 0.90

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DagsHub

JUNE 3, 2024

The pedestrian died, and investigators found that there was an issue with the machine learning (ML) model in the car, so it failed to identify the pedestrian beforehand. Therefore, let’s examine how you can improve the overall accuracy of your machine learning models so that they perform well and make reliable and safe predictions.

Machine Learning 52

Machine Learning Machine Learning Decision Trees ML 52

Pickl AI

DECEMBER 10, 2024

Summary: Hyperparameters in Machine Learning are essential for optimising model performance. They are set before training and influence learning rate and batch size. This summary explores hyperparameter categories, tuning techniques, and tools, emphasising their significance in the growing Machine Learning landscape.

Machine Learning 52

Machine Learning Machine Learning Cross Validation Decision Trees 52

Pickl AI

NOVEMBER 18, 2024

Summary: The blog provides a comprehensive overview of Machine Learning Models, emphasising their significance in modern technology. It covers types of Machine Learning, key concepts, and essential steps for building effective models. The global Machine Learning market was valued at USD 35.80

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Pickl AI

JANUARY 8, 2025

Summary : Feature selection in Machine Learning identifies and prioritises relevant features to improve model accuracy, reduce overfitting, and enhance computational efficiency. Introduction Feature selection in Machine Learning is identifying and selecting the most relevant features from a dataset to build efficient predictive models.

Machine Learning 52

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Pickl AI

JULY 26, 2023

The concepts of bias and variance in Machine Learning are two crucial aspects in the realm of statistical modelling and machine learning. Understanding these concepts is paramount for any data scientist, machine learning engineer, or researcher striving to build robust and accurate models.

Machine Learning 52

Machine Learning Machine Learning Cross Validation Decision Trees 52

Pickl AI

JUNE 4, 2024

Summary : Building a machine learning model is just one step. Validating its performance on unseen data is crucial. Python offers various tools like train-test split and cross-validation to assess model generalizability. K-Fold Cross-Validation In k-fold cross-validation, the data is divided into k subsets.

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Mlearning.ai

FEBRUARY 15, 2023

Figure 1 Preprocessing Data preprocessing is an essential step in building a Machine Learning model. Some important things that were considered during these selections were: Random Forest : The ultimate feature importance in a Random forest is the average of all decision tree feature importance. link] Ganaie, M.

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Pickl AI

JANUARY 3, 2024

Feature engineering in machine learning is a pivotal process that transforms raw data into a format comprehensible to algorithms. Embrace the benefits of feature engineering to unlock the full potential of your Machine-Learning endeavors and achieve accurate predictions in diverse real-world scenarios.

Machine Learning 52

Machine Learning Machine Learning Exploratory Data Analysis Cross Validation 52

Pickl AI

FEBRUARY 19, 2025

Summary: Boosting in Machine Learning improves predictive accuracy by sequentially training weak models. Despite computational costs, Boosting remains vital for handling complex data and optimising AI models for high-performance decision-making. Introduction Boosting in Machine Learning is a powerful ensemble technique.

Machine Learning 52

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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. billion in 2022 and is expected to grow to USD 505.42

Machine Learning 52

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Pickl AI

MAY 17, 2023

Machine learning empowers the machine to perform the task autonomously and evolve based on the available data. However, while working on a Machine Learning algorithm , one may come across the problem of underfitting or overfitting. Both these aspects can impact the performance of the Machine Learning model.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Smart Data Collective

JULY 1, 2023

Image annotation is the act of labeling images for AI and machine learning models. The resulting structured data is then used to train a machine learning algorithm. There are a lot of image annotation techniques that can make the process more efficient with deep learning.

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DrivenData Labs

MAY 22, 2024

I saw this as an exciting opportunity to test and expand my machine learning skills in a practical, real-world setting. Also, I have 10 years of experience with C++ cross-platform development, especially in the medical imaging domain, and for embedded solutions. quantile forecast. What motivated you to compete in this challenge?

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Mlearning.ai

NOVEMBER 29, 2023

How to Use Machine Learning (ML) for Time Series Forecasting — NIX United The modern market pace calls for a respective competitive edge. Data forecasting has come a long way since formidable data processing-boosting technologies such as machine learning were introduced. Some of them may even be deemed outdated by now.

Machine Learning 52

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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. Machine learning algorithms rely on mathematical functions called “kernels” to make predictions based on input data. What are kernels? Linear Kernels 2.

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Ocean Protocol

NOVEMBER 28, 2024

Participants used historical data from past Mexican Grand Prix events and insights from the 2024 F1 season to create machine-learning models capable of predicting key race elements. With every second on the track critical, the challenge showcased how data can shape decisions that define race outcomes.

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Towards AI

JULY 19, 2023

Check out the previous post to get a primer on the terms used) Outline Dealing with Class Imbalance Choosing a Machine Learning model Measures of Performance Data Preparation Stratified k-fold Cross-Validation Model Building Consolidating Results 1. among supervised models and k-nearest neighbors, DBSCAN, etc.,

Machine Learning 40

Machine Learning Machine Learning K-nearest Neighbors Support Vector Machines 40

Pickl AI

JULY 12, 2024

Summary: This guide explores Artificial Intelligence Using Python, from essential libraries like NumPy and Pandas to advanced techniques in machine learning and deep learning. Introduction Artificial Intelligence (AI) transforms industries by enabling machines to mimic human intelligence.

Artificial Intelligence 52

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Pickl AI

SEPTEMBER 12, 2024

By understanding crucial concepts like Machine Learning, Data Mining, and Predictive Modelling, analysts can communicate effectively, collaborate with cross-functional teams, and make informed decisions that drive business success. Data Cleaning: Raw data often contains errors, inconsistencies, and missing values.

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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. Explain the bias-variance tradeoff in Machine Learning. Here is a brief description of the same.

Data Science 52

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Heartbeat

OCTOBER 31, 2023

The goal of bagging is to enhance the performance and accuracy of machine learning models. Before continuing, revisit the lesson on decision trees if you need help understanding what they are. Cross-validation is recommended as best practice to provide reliable results because of this.

Decision Trees 52

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Mlearning.ai

FEBRUARY 10, 2024

Unlocking Predictive Power: How Bayes’ Theorem Fuels Naive Bayes Algorithm to Solve Real-World Problems [link] Introduction In the constantly shifting realm of machine learning, we can see that many intricate algorithms are rooted in the fundamental principles of statistics and probability.

Algorithm 52

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Pickl AI

DECEMBER 12, 2024

Summary: XGBoost is a highly efficient and scalable Machine Learning algorithm. Introduction Boosting is a powerful Machine Learning ensemble technique that combines multiple weak learners, typically decision trees, to form a strong predictive model. It reduces overfitting using L1 and L2 penalties.

Machine Learning 52

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Mlearning.ai

FEBRUARY 15, 2024

This cross-validation results shows without regularization. Decision Tree This will create a predictive model based on simple if-else decisions. So far, the Decision tree classifier model with max_depth =10 and the min_sample_split = 0.005 has given the best result. Why am I using regularization?

Data Science 52

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Pickl AI

MAY 24, 2023

This may include for instance in Machine Learning, Data Science, Data Visualisation, image and Data Manipulation. As the collection of Python libraries are huge, you might be faced with the need to make difficult decision on which library to choose from. What to consider when choosing a Python Library?

Data Science 52

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Mlearning.ai

MAY 23, 2023

An interdisciplinary field that constitutes various scientific processes, algorithms, tools, and machine learning techniques working to help find common patterns and gather sensible insights from the given raw input data using statistical and mathematical analysis is called Data Science. Decision trees are more prone to overfitting.

Data Science 52

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DagsHub

APRIL 21, 2024

Source: [link] Similarly, while building any machine learning-based product or service, training and evaluating the model on a few real-world samples does not necessarily mean the end of your responsibilities. MLOps tools play a pivotal role in every stage of the machine learning lifecycle. What is MLOps?

Machine Learning 59

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Pickl AI

AUGUST 9, 2024

Machine Learning Algorithms Basic understanding of Machine Learning concepts and algorithm s, including supervised and unsupervised learning techniques. Students should learn how to apply machine learning models to Big Data. Students should learn about neural networks and their architecture.

Big Data 52

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Pickl AI

APRIL 26, 2024

It covers essential topics such as SQL queries, data visualization, statistical analysis, machine learning concepts, and data manipulation techniques. Statistical Analysis: Learn the Central Limit Theorem, correlation, and basic calculations like mean, median, and mode. The median is the middle value in a sorted list of numbers.

Data Analyst 52

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Iguazio

JUNE 25, 2023

Some of the most common performance metrics for machine learning models include: Classification Model Metrics A classification model is a model that is trained to assign class labels to input data based on certain patterns or features. They vary according to the model type and use cases.

ML 52

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Pickl AI

NOVEMBER 2, 2023

A cheat sheet for Data Scientists is a concise reference guide, summarizing key concepts, formulas, and best practices in Data Analysis, statistics, and Machine Learning. It serves as a handy quick-reference tool to assist data professionals in their work, aiding in data interpretation, modeling , and decision-making processes.

Data Scientist 52

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Heartbeat

MAY 29, 2023

We will also discuss best practices for training LLMs, such as using transfer learning, data augmentation, and ensembling methods. LLMs use a combination of machine learning and human input; image from OpenAI Data preparation and preprocessing The first, and perhaps most crucial, step in LLM training is data preparation.

Machine Learning 78

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PyImageSearch

JANUARY 23, 2023

The reasoning behind that is simple; whatever we have learned till now, be it adaptive boosting, decision trees, or gradient boosting, have very distinct statistical foundations which require you to get your hands dirty with the math behind them. The goal is to nullify the abstraction created by packages as much as possible.

Deep Learning 92

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Pickl AI

FEBRUARY 4, 2024

Predictive Analytics: Leverage machine learning algorithms for accurate predictions. This makes Alteryx an indispensable tool for businesses aiming to glean insights and steer their decisions based on robust data. Predictive modeling Alteryx elevates predictive modeling with integrated machine learning algorithms and AutoML.

Data Preparation 52

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Dataconomy

SEPTEMBER 1, 2023

Apart from many areas in our lives, hybrid machine learning techniques can help us with effective heart disease prediction. So how can the technology of our time, machine learning, be used to improve the quality and length of human life? According to the World Health Organization , heart disease takes an estimated 17.9

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