PickAClass · AI, Tech & Engineering · AI & Machine Learning

Supervised Learning

Master the most common type of machine learning. Learn to build models that make predictions based on labeled data, covering regression and classification tasks.

Supervised Learning

Categorical Feature Engineering for Machine Learning

Linear Regression Challenges and Solutions with Scikit-Learn

Machine Learning Data Collection and Engineering Strategies

Introduction to Linear Classifiers in Machine Learning

Machine Learning Model Evaluation and Metrics

Feature Selection with Embedded Machine Learning Models

Predicting Pizza Sales with Supervised Machine Learning

Gradient Boosting Trees in Scikit-Learn: A Practical Guide

Gradient Descent for Binary Classification Models

Designing Feed Ranking Models and Training Data Strategies

Feature Scaling and Data Preprocessing for Binary Classification

Nearest Neighbor Regression: Machine Learning Foundations

Logistic Regression for Classification: A Practical Guide

Machine Learning Model Evaluation and Optimization

Evaluating Regression Models: Key Machine Learning Metrics

Understanding Naive Bayes Classifiers in Machine Learning

Working with Built-In Datasets in Scikit-Learn

Testing Machine Learning Models with Real-World Data

Low-Code Machine Learning: Regression Modeling with PyCaret

PyTorch Classification: Building Logistic Regression Models

Implementing Linear Regression: Data Prep and Setup in Python

Feature Selection: Filter Methods for Machine Learning

Dataset Preparation: Merging Data for Machine Learning Models

PySpark for Machine Learning: Hands-On Foundations