Pickl AI

NOVEMBER 18, 2024

The article also addresses challenges like data quality and model complexity, highlighting the importance of ethical considerations in Machine Learning applications. Key steps involve problem definition, data preparation, and algorithm selection. Data quality significantly impacts model performance.

Machine Learning 52

Machine Learning Machine Learning Decision Trees Algorithm 52

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.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

JULY 12, 2024

Jupyter notebooks are widely used in AI for prototyping, data visualisation, and collaborative work. Their interactive nature makes them suitable for experimenting with AI algorithms and analysing data. Importance of Data in AI Quality data is the lifeblood of AI models, directly influencing their performance and reliability.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Heartbeat

OCTOBER 23, 2023

The Role of Data Scientists and ML Engineers in Health Informatics At the heart of the Age of Health Informatics are data scientists and ML engineers who play a critical role in harnessing the power of data and developing intelligent algorithms.

Machine Learning 52

Machine Learning Machine Learning Data Scientist Big Data Analytics 52

Pickl AI

DECEMBER 16, 2024

Summary: AI in Time Series Forecasting revolutionizes predictive analytics by leveraging advanced algorithms to identify patterns and trends in temporal data. Advanced algorithms recognize patterns in temporal data effectively. This step includes: Identifying Data Sources: Determine where data will be sourced from (e.g.,

AI 52

AI AI Machine Learning Machine Learning 52

Pickl AI

SEPTEMBER 12, 2024

Key Components of Data Science Data Science consists of several key components that work together to extract meaningful insights from data: Data Collection: This involves gathering relevant data from various sources, such as databases, APIs, and web scraping.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Pickl AI

AUGUST 9, 2024

Students should learn about data wrangling and the importance of data quality. Statistical Analysis Introducing statistical methods and techniques for analysing data, including hypothesis testing, regression analysis, and descriptive statistics. Students should learn how to apply machine learning models to Big Data.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Mlearning.ai

NOVEMBER 29, 2023

All the previously, recently, and currently collected data is used as input for time series forecasting where future trends, seasonal changes, irregularities, and such are elaborated based on complex math-driven algorithms. This results in quite efficient sales data predictions. In its core, lie gradient-boosted decision trees.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Heartbeat

MAY 29, 2023

BERT model architecture; image from TDS Hyperparameter tuning Hyperparameter tuning is the process of selecting the optimal hyperparameters for a machine learning algorithm. Conversely, a smaller batch size can lead to slower convergence but can be more memory-efficient and may generalize better to new data.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78

Pickl AI

OCTOBER 15, 2024

These models do not rely on predefined labels; instead, they discover the inherent structure in the data by identifying clusters based on similarities. Popular clustering algorithms include k-means and hierarchical clustering. Quality data is essential, as poor or incomplete data can lead to inaccurate models.

Decision Trees 52

Decision Trees Hypothesis Testing Clustering Data Analysis 52

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. In my previous role, we had a project with a tight deadline.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

DagsHub

MARCH 5, 2024

Using various algorithms and tools, a computer vision model can extract valuable information and make decisions by analyzing digital content like images and videos. Thorough validation procedures: Evaluate model performance on unseen data during validation, resembling real-world distribution.

Cross Validation 52

Cross Validation Algorithm Data Pipeline Data Preparation 52

Mlearning.ai

JUNE 6, 2023

Democratizing Machine Learning Machine learning entails a complex series of steps, including data preprocessing, feature engineering, algorithm selection, hyperparameter tuning, and model evaluation. AutoML leverages the power of artificial intelligence and machine learning algorithms to automate the machine learning pipeline.

Machine Learning 52

Machine Learning Machine Learning Algorithm Data Quality 52

Dataconomy

MARCH 10, 2025

Understanding its role can enhance the effectiveness of machine learning algorithms, ensuring they make accurate predictions and decisions based on real-world data. Ground truth in machine learning refers to the precise, labeled data that provides a benchmark for various algorithms.

Machine Learning 91

Machine Learning Machine Learning Algorithm Cross Validation 91