Pickl AI

FEBRUARY 12, 2024

Machine Learning Algorithms Candidates should demonstrate proficiency in a variety of Machine Learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

Pickl AI

JULY 31, 2023

Understanding Eager Learning Eager Learning, also known as “Eager Supervised Learning,” is a widely used approach in Machine Learning. It learns the relationship between features and class labels during training and then predicts the probability of an instance belonging to a specific class.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Algorithm 52

phData

AUGUST 24, 2023

With a modeled estimation of the applicant’s credit risk, lenders can make more informed decisions and reduce the occurrence of bad loans, thereby protecting their bottom line. Now that we have a firm grasp on the underlying business case, we will now define a machine learning pipeline in the context of credit models.

ML 52

ML ML Machine Learning Machine Learning 52

Heartbeat

OCTOBER 11, 2023

Supervised, unsupervised, and reinforcement learning : Machine learning can be categorized into different types based on the learning approach. Model Complexity Machine Learning : Traditional machine learning models have fewer parameters and a simpler structure than deep learning models.

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

Pickl AI

MAY 31, 2023

Explore Machine Learning with Python: Become familiar with prominent Python artificial intelligence libraries such as sci-kit-learn and TensorFlow. Begin by employing algorithms for supervised learning such as linear regression , logistic regression, decision trees, and support vector machines.

Data Science 52

Data Science Python Data Scientist Machine Learning 52

Dataconomy

MAY 25, 2023

Here are some important machine learning techniques used in IoT: Supervised learning Supervised learning involves training machine learning models with labeled datasets.

Internet of Things 160

Internet of Things Artificial Intelligence Artificial Intelligence AI 160

IBM Journey to AI blog

JULY 6, 2023

Subcategories of machine learning Some of the most commonly used machine learning algorithms include linear regression , logistic regression, decision tree , Support Vector Machine (SVM) algorithm, Naïve Bayes algorithm and KNN algorithm.

Machine Learning 60

Machine Learning Machine Learning Data Science Big Data 60

Dataconomy

AUGUST 31, 2023

Multi-class classification in machine learning Multi-class classification in machine learning is a type of supervised learning problem where the goal is to predict one of multiple classes or categories based on input features.

Machine Learning 136

Machine Learning Machine Learning Supervised Learning Algorithm 136

Dataconomy

APRIL 4, 2023

Reminder : Training data refers to the data used to train an AI model, and commonly there are three techniques for it: Supervised learning: The AI model learns from labeled data, which means that each data point has a known output or target value. 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

Reminder : Training data refers to the data used to train an AI model, and commonly there are three techniques for it: Supervised learning: The AI model learns from labeled data, which means that each data point has a known output or target value. Let’s dig deeper and learn more about them!

K-nearest Neighbors 91

K-nearest Neighbors Decision Trees AI AI 91

IBM Journey to AI blog

DECEMBER 20, 2023

Machine learning types Machine learning algorithms fall into five broad categories: supervised learning, unsupervised learning, semi-supervised learning, self-supervised and reinforcement learning. the target or outcome variable is known). temperature, salary).

Machine Learning 82

Machine Learning Machine Learning Supervised Learning Clustering 82

Dataconomy

MARCH 13, 2023

There are several types of AI algorithms, including supervised learning, unsupervised learning, and reinforcement learning. Models:  AI models are mathematical representations of a system that can make predictions or decisions based on the input data.

Artificial Intelligence 98

Artificial Intelligence Artificial Intelligence Natural Language Processing Algorithm 98

Mlearning.ai

APRIL 13, 2023

Before we discuss the above related to kernels in machine learning, let’s first go over a few basic concepts: Support Vector Machine , S upport Vectors and Linearly vs. Non-linearly Separable Data. Support-vector networks. References [1] Cortes, C., & Vapnik, V. Why is it important? — Medium

Machine Learning 52

Machine Learning Machine Learning Data Scientist Support Vector Machines 52