IBM Journey to AI blog

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. What is machine learning?

Machine Learning 97

Machine Learning Machine Learning Supervised Learning Clustering 97

Pickl AI

SEPTEMBER 23, 2024

Machine Learning Machine Learning (ML) is a crucial component of Data Science. ML models help predict outcomes, automate tasks, and improve decision-making by identifying patterns in large datasets. Unsupervised Learning techniques such as clustering and dimensionality reduction to discover patterns in data.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Data Science Machine Learning 52

Dataconomy

MAY 25, 2023

ML algorithms for analyzing IoT data using artificial intelligence Machine learning forms the foundation of AI in IoT, allowing devices to learn patterns, make predictions, and adapt to changing circumstances. Unsupervised learning Unsupervised learning involves training machine learning models with unlabeled datasets.

Internet of Things 160

Internet of Things Artificial Intelligence Artificial Intelligence AI 160

Pickl AI

JULY 12, 2024

Here are a few of the key concepts that you should know: Machine Learning (ML) This is a type of AI that allows computers to learn without being explicitly programmed. Machine Learning algorithms are trained on large amounts of data, and they can then use that data to make predictions or decisions about new data.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Dataconomy

APRIL 4, 2023

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? Let’s dig deeper and learn more about them!

K-nearest Neighbors 91

K-nearest Neighbors Decision Trees AI AI 91

Dataconomy

APRIL 4, 2023

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? Let’s dig deeper and learn more about them!

K-nearest Neighbors 91

K-nearest Neighbors Decision Trees AI AI 91

Dataconomy

MARCH 13, 2023

Scikit-learn:  Scikit-learn is an open-source library that provides a range of tools for building and training machine learning models, including classification, regression, and clustering. Algorithm selection:  Choose algorithms that are less prone to biases, such as decision trees or support vector machines.

Artificial Intelligence 98

Artificial Intelligence Artificial Intelligence Natural Language Processing Algorithm 98

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. You need to make that model available to the end users, monitor it, and retrain it for better performance if needed. What is MLOps?

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Mlearning.ai

MAY 23, 2023

There are majorly two categories of sampling techniques based on the usage of statistics, they are: Probability Sampling techniques: Clustered sampling, Simple random sampling, and Stratified sampling. Decision trees are more prone to overfitting. Some algorithms that have low bias are Decision Trees, SVM, etc.

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52