IBM Journey to AI blog

DECEMBER 20, 2023

Classification algorithms include logistic regression, k-nearest neighbors and support vector machines (SVMs), among others. Naïve Bayes algorithms include decision trees , which can actually accommodate both regression and classification algorithms.

Machine Learning 97

Machine Learning Machine Learning Supervised Learning Clustering 97

Towards AI

JULY 19, 2023

That post was dedicated to an exploratory data analysis while this post is geared towards building prediction models. Feel free to try other algorithms such as Random Forests, Decision Trees, Neural Networks, etc., among supervised models and k-nearest neighbors, DBSCAN, etc., among unsupervised models.

Machine Learning 40

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

Pickl AI

JULY 12, 2024

Data Normalization and Standardization: Scaling numerical data to a standard range to ensure fairness in model training. Exploratory Data Analysis (EDA) EDA is a crucial preliminary step in understanding the characteristics of the dataset. classification, regression) and data characteristics.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

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. Here is a brief description of the same.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

Pickl AI

JANUARY 29, 2024

Key Components In Data Science, key components include data cleaning, Exploratory Data Analysis, and model building using statistical techniques. ML focuses on algorithms like decision trees, neural networks, and support vector machines for pattern recognition.

Data Science 52

Data Science ML ML Machine Learning 52

Pickl AI

SEPTEMBER 12, 2024

Data Wrangling: The cleaning, transforming, and structuring of raw data into a format suitable for analysis. Decision Trees: A supervised learning algorithm that creates a tree-like model of decisions and their possible consequences, used for both classification and regression tasks.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Heartbeat

SEPTEMBER 28, 2023

In a typical MLOps project, similar scheduling is essential to handle new data and track model performance continuously. Load and Explore Data We load the Telco Customer Churn dataset and perform exploratory data analysis (EDA). Random Forest Classifier (rf): Ensemble method combining multiple decision trees.

Support Vector Machines 52

Support Vector Machines Machine Learning Machine Learning ML 52