Classification Algorithms Courses and Certifications

Data Science: Supervised Machine Learning in Python

3555 ratings at Udemy

Full Guide to Implementing Classic Machine Learning Algorithms in Python and with Scikit-Learn

• Udemy
• 6 hours 25 minutes
• Self-Paced
• Paid Course

Coursera

Scikit-Learn For Machine Learning Classification Problems

20 ratings at Coursera

Build and train classifier models with the Scikit-Learn library in Python, working with algorithms such as support vector machines, random forests, and gradient boosting.

• Coursera
• 1 hour 48 minutes
• Self-Paced
• Paid Course

Machine Learning Fundamentals

Learn supervised machine learning: build linear regression models with regularization, separate data using discriminant functions and probabilistic models, and compare classifiers with ROC curves.

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

Machine Learning and Data Analytics Part 1

Apply data mining in engineering: run exploratory analysis, reduce dimensions with PCA and t-SNE, build classification and regression models, and evaluate them using RMSE and AUC-ROC.

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

EDUCBA

Machine Learning with Python: Case Studies

Apply regression, k-means clustering, and classifiers in Python across case studies on salary prediction, Bitcoin forecasting, face detection, and credit card default risk.

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

Abhishek Thakur

Applied Machine Learning Framework

Master building a comprehensive ML framework from cross-validation to categorical features, binary classification metrics, and entity embeddings for real-world applications.

• YouTube
• 5 hours 22 minutes
• Self-Paced
• Free Video

Great Learning

Machine Learning Concepts - Comprehensive Tutorial Series

Discover essential machine learning algorithms, from linear regression to neural networks, with hands-on Python implementation and real-world applications.

• YouTube
• 1 day 14 hours 35 minutes
• Self-Paced
• Free Video

NPTEL-NOC IITM

Logistic Regression - Continued

Explore advanced concepts and applications of logistic regression for enhanced predictive modeling and data analysis skills.

• YouTube
• 19 minutes
• Self-Paced
• Free Video

NPTEL-NOC IITM

Logistic Regression: Introduction to Binary Classification and Linear Classifiers

Master logistic regression for binary classification, covering linear classifiers, sigmoid functions, parameter estimation, and log-likelihood functions.

• YouTube
• 29 minutes
• Self-Paced
• Free Video

NPTEL-NOC IITM

K-Nearest Neighbors Implementation in R

Implement K-Nearest Neighbors algorithm in R programming language. Learn practical application of this machine learning technique for classification and regression tasks.

• YouTube
• 29 minutes
• Self-Paced
• Free Video

Python Tutorials for Digital Humanities

Machine Learning for Digital Humanities

Discover machine learning and neural networks designed specifically for humanities scholars without mathematical backgrounds using TensorFlow and Keras.

• YouTube
• 2 hours 58 minutes
• Self-Paced
• Free Video

Python Tutorials for Digital Humanities

Neural Networks for Digital Humanities

Discover how to apply neural networks and deep learning to humanities research using TensorFlow and Keras, designed specifically for non-mathematical backgrounds.

• YouTube
• 2 hours 58 minutes
• Self-Paced
• Free Video

Alfredo Canziani

NYU Artificial Intelligence - Introduction to Machine Learning Fundamentals

Discover fundamental AI concepts including Naïve Bayes classification, perceptron algorithms, logistic regression, and optimization techniques through NYU's comprehensive curriculum.

• YouTube
• 6 hours 11 minutes
• Self-Paced
• Free Video

Great Learning

Classification with Decision Trees

Learn how decision trees implement classification in R, including tree-based models, impurity measures, and decision tree algorithms.

• YouTube
• 2 hours 14 minutes
• Self-Paced
• Free Video

NPTEL-NOC IITM

Neural Networks: Introduction to Artificial Neural Networks and Multilayer Perceptrons

Master the fundamentals of Neural Networks, including neuron structure, ANN components, training processes, and classification techniques.

• YouTube
• 35 minutes
• Self-Paced
• Free Video