Principal Component Analysis Courses and Certifications

Statistics for Applications

1 review

This course offers an in-depth the theoretical foundations for statistical methods that are useful in many applications.

• MIT OpenCourseWare
• 28 hours
• On-Demand
• Free Video

Statistics with R - Advanced Level

222 ratings at Udemy

Mastering advanced statistical analyses in R: from ANCOVA to logistic regression, multidimensional scaling, factor analysis, cluster analysis, and discriminant analysis. Gain expertise in sophisticated data scrutiny techniques.

• Udemy
• 4 hours 15 minutes
• On-Demand
• Paid Course

Clustering Analysis

11 ratings at Coursera

Explore unsupervised learning techniques, including clustering methods and dimension reduction, to uncover patterns and insights in diverse datasets through hands-on tutorials and case studies.

• Coursera
• 1 day 13 hours 2 minutes
• On-Demand
• Paid Course

Resampling, Selection and Splines

Explore advanced statistical learning techniques for data science, including resampling methods, model optimization, and non-linear approaches to enhance prediction accuracy and interpretability.

• Coursera
• 15 hours 56 minutes
• On-Demand
• Paid Course

Data Pre-Processing for Data Analytics and Data Science

134 ratings at Udemy

Pre-Processing for Data Analytics and Data Science

• Udemy
• 8 hours 52 minutes
• On-Demand
• Paid Course

Feature Engineering For Machine Learning 101

122 ratings at Udemy

Feature Engineering | Machine Learning | Artificial Intelligence | Chat GPT | LLM | generative ai | Manus | Ai Agent

• Udemy
• 2 hours 9 minutes
• On-Demand
• Paid Course

Machine Learning for Interviews & Research and DL basics

123 ratings at Udemy

Machine Learning, Linear Regression, PCA, Neural Networks, Hyperparameters, Deep Learning, Keras, Clustering, Case Study

• Udemy
• 4 hours 52 minutes
• On-Demand
• Paid Course

Numerical Methods and Optimization in Python

115 ratings at Udemy

Gaussian Elimination, Eigenvalues, Numerical Integration, Interpolation, Differential Equations and Operations Research

• Udemy
• 14 hours 3 minutes
• On-Demand
• Paid Course

Data Mining - Unsupervised Learning

117 ratings at Udemy

Master unsupervised learning techniques including clustering, dimensionality reduction, and association rules. Apply practical algorithms for pattern discovery, anomaly detection, and recommendation systems using Python and R.

• Udemy
• 10 hours 42 minutes
• On-Demand
• Paid Course

Machine Learning for Beginner (AI) - Data Science

56 ratings at Udemy

Learn Machine Learning from scratch. Mathematical & Graphical explanation, Python projects and ebooks

• Udemy
• 6 hours 52 minutes
• On-Demand
• Paid Course

Whizlabs

Data Engineering in AWS

26 ratings at Coursera

Learn data engineering techniques for AWS, including data gathering, handling missing data, feature extraction, and selection using PCA and variance thresholds. Gain hands-on experience with SageMaker and AWS migration tools.

• Coursera
• 4 hours 50 minutes
• On-Demand
• Paid Course

Machine Learning and Data Analytics Part 1

Dive into data mining fundamentals, exploring preprocessing, classification, clustering, and regression techniques through hands-on projects and real-world case studies.

• Coursera
• 15 hours 20 minutes
• On-Demand
• Paid Course

Practical Engineering Data Mining: Techniques and Uses

Master data mining fundamentals and techniques through hands-on engineering applications, from preprocessing to clustering, while implementing real-world case studies across manufacturing, healthcare, and business sectors.

• Coursera
• 11 hours 7 minutes
• On-Demand
• Paid Course

Unsupervised Learning and Its Applications in Marketing

Explore unsupervised learning techniques in Python for marketing insights. Uncover customer segments, reduce data dimensions, detect anomalies, and build recommender systems to drive data-driven marketing strategies.

• Coursera
• 21 hours 43 minutes
• On-Demand
• Paid Course

Coursera

Advanced Machine Learning Techniques

Master ensemble methods, dimensionality reduction, NLP, reinforcement learning, and AutoML using industry-standard tools like Scikit-learn, XGBoost, and PyTorch for real-world AI applications.

• Coursera
• 1 day 9 hours 42 minutes
• On-Demand
• Paid Course