Logistic Regression Courses and Certifications

Preparing Your Data and Setting a BaseLine

Master data preprocessing fundamentals: load datasets with Pandas, visualize patterns, handle missing values, encode categorical variables, and establish baseline model performance.

• CodeSignal
• 2 hours
• Self-Paced
• Free Certificate

Handling Unbalanced Datasets

Master practical techniques to identify class imbalance and apply undersampling, oversampling, and advanced resampling strategies for better model performance on imbalanced data.

• CodeSignal
• 2 hours
• Self-Paced
• Free Certificate

Evaluation Metrics & Advanced Techniques for Imbalanced Data

Master evaluation metrics and advanced techniques for handling imbalanced datasets, including cost-sensitive learning, ensemble methods, and anomaly detection strategies.

• CodeSignal
• 2 hours
• Self-Paced
• Free Certificate

Regression and Gradient Descent

Master regression algorithms and gradient descent by building them from scratch, covering linear and logistic regression without relying on high-level libraries.

• CodeSignal
• 3 hours
• Self-Paced
• Free Certificate

Classification Algorithms and Metrics

Dive into classification algorithms by building Logistic Regression, k-NN, Naive Bayes, and Decision Trees from scratch without libraries, plus implement AUCROC metrics.

• CodeSignal
• 5 hours
• Self-Paced
• Free Certificate

Navigating Data Simplification with PCA

Master dimensionality reduction techniques using Principal Component Analysis in R, from understanding eigenvectors to implementing PCA for feature extraction and classification tasks.

• CodeSignal
• 3 hours
• Self-Paced
• Free Certificate

Linear Landscapes of Dimensionality Reduction

Discover Linear Discriminant Analysis techniques for dimensionality reduction and feature selection through hands-on R programming exercises with real datasets.

• CodeSignal
• 2 hours
• Self-Paced
• Free Certificate

Learning to Predict User Preferences

Master supervised machine learning to build advanced recommendation engines using user data, logistic regression, and predictive scoring for personalized music suggestions.

• CodeSignal
• 2 hours
• Self-Paced
• Free Certificate

Python Machine Learning

Master machine learning fundamentals through hands-on Python programming, covering regression, classification, decision trees, and ensemble methods to solve real-world data problems.

• VDCI
• 30 hours
• Self-Paced
• $1,895.00

Natural Language Processing with Classification and Vector Spaces

4638 ratings at Coursera

This is the Course 1 of the Natural Language Processing Specialization, offered by deeplearning.ai

• Coursera
• 1 day 9 hours 23 minutes
• Self-Paced
• Paid Course

Credential

Statistical Analysis with R for Public Health

2081 ratings at Coursera

Learn statistical analysis with R for public health applications. Covers key concepts, data analysis techniques, and hands-on practice using real-world health datasets to address major public health challenges.

• Coursera
• 4 weeks, 10 hours/week
• Self-Paced
• Paid Course
• 4 courses

Simple Regression Analysis in Public Health

391 ratings at Coursera

Learn to apply simple regression methods in public health, including linear, logistic, and Cox regression. Practice interpreting data and performing calculations using real-world examples.

• Coursera
• 13 hours 56 minutes
• Self-Paced
• Paid Course

Multiple Regression Analysis in Public Health

328 ratings at Coursera

Extend simple regression to predict outcomes using multiple variables. Practice interpreting data and performing calculations on real studies in public health and biostatistics.

• Coursera
• 14 hours
• Self-Paced
• Paid Course

Introduction to Neural Networks and PyTorch

1901 ratings at Coursera

Learn to build and train deep neural networks using PyTorch, covering tensors, linear regression, logistic regression, CNNs, and transfer learning for practical AI applications.

• Coursera
• 18 hours 4 minutes
• Self-Paced
• Paid Course

Statistical Learning with Python

24 ratings at edX

Learn some of the main tools used in statistical modeling and data science. We cover both traditional as well as exciting new methods, and how to use them in Python.

• edX
• 11 weeks, 3-5 hours/week
• Self-Paced
• Free Online Course (Audit)