Supervised Learning Courses and Certifications

CertNexus

Train Machine Learning Models

Train, tune, and evaluate classification, regression, and clustering models, testing model hypotheses with design of experiments and applying algorithms in a Jupyter notebook.

• Coursera
• 5 weeks, 6 hours a week
• Self-Paced
• Paid Course

Coursera

AI-Driven Financial Planning, Forecasting, and Automation

Build zero-based budgets, multi-year P&L projections, and stress-test scenarios, then forecast metrics with supervised learning, SHAP explanations, and automated SEC-filing pipelines.

• Coursera
• 11 weeks, 1 hour a week
• Self-Paced
• Paid Course

Edureka

Applied Machine Learning with Python

Build machine learning models in Python: decision trees and random forests for classification, regression for prediction, K-means clustering, boosting, and association rule mining for recommendations.

• Coursera
• 4 weeks, 4 hours a week
• Self-Paced
• Paid Course

Statistical Analysis and Data Modeling in Healthcare

Analyze healthcare datasets in Python and Google Colab: compute descriptive statistics, run t-tests and ANOVA, build linear, logistic, and tree-based predictive models, and validate results.

• Coursera
• 4 weeks, 2 hours a week
• Self-Paced
• Paid Course

Coursera

Decision Tree Classifier for Beginners in R

Build decision tree classifiers in R with the tree and rpart libraries: import the Sonar data, split train/test sets, and score accuracy, sensitivity, specificity and F1.

• Coursera
• 2 hours 15 minutes
• Self-Paced
• Paid Course

Coursera

Statistical and Predictive Modeling for Finance

Apply regression, statistics and supervised learning to finance: compute alpha and beta, diagnose residuals in RStudio, design A/B tests, and score credit risk with decision trees.

• Coursera
• 11 weeks, 1 hour a week
• Self-Paced
• Paid Course

Birla Institute Of Technology And Science–Pilani (BITS–Pilani)

Introduction to Machine Learning

Learn core machine learning methods — linear and logistic regression, Naive Bayes, decision trees, kernel SVMs and k-NN — with regularisation, bias-variance tradeoff and evaluation metrics.

• Coursera
• 10 weeks, 5 hours a week
• Self-Paced
• Paid Course

Coursera Career Certificate

Machine Learning Engineer: ML and Deep Learning Models

Turn real problems into machine learning tasks: build supervised models, design custom neural networks in PyTorch, and tune, debug, and optimize deep learning models.

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

Coursera

Solving ML Regression Problems with AWS AutoGluon

Train several machine learning regression models with AutoGluon, the AutoML library behind AWS SageMaker Autopilot, prototyping powerful models in a few lines of code.

• Coursera
• 1 hour 54 minutes
• Self-Paced
• Paid Course

Packt

Intermediate Data Manipulation and Machine Learning

Apply regression, regularization, decision trees, random forests, SVMs, clustering, PCA and t-SNE, Apriori association rules, and reinforcement learning through hands-on Python labs.

• Coursera
• 14 weeks, 1 hour a week
• Self-Paced
• Paid Course

Coursera

Forecast Business Metrics: Uncover Value Drivers

Build and tune regression and gradient-boosted models in Python to forecast metrics like next-quarter EBITDA, then use SHAP and feature importance to explain the drivers.

• Coursera
• 2 hours 7 minutes
• Self-Paced
• Paid Course

Packt

Clustering and Classification with Machine Learning in R

Build clustering and classification models in R: import and clean data with dplyr and ggplot2, then apply K-means, DBSCAN, PCA, Random Forests and SVM.

• Coursera
• 10 weeks, 1 hour a week
• Self-Paced
• Paid Course

EDUCBA

Logistic Regression Fundamentals: Analyze & Predict

Build and evaluate logistic regression models for binary outcomes: logit transformation, odds interpretation, maximum likelihood estimation, binning and dummy variables, and SAS PROC LOGISTIC diagnostics.

• Coursera
• 2 weeks, 2 hours a week
• Self-Paced
• Paid Course

Coursera Career Certificate

Data Preparation & Applied Machine Learning

Clean messy datasets, combine sources, handle missing and inconsistent values, diagnose data quality issues, and build supervised models for classification, regression, and forecasting.

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

EDUCBA

Logistic Regression with R: Build & Predict

Build logistic regression models in R: prepare data, interpret coefficients, and evaluate with confusion matrices, ROC and AUC across diabetes, credit risk and loan approval datasets.

• Coursera
• 3 weeks, 3 hours a week
• Self-Paced
• Paid Course