When to Use Machine Learning

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (32)

Data Representations and Processing in Machine Learning

Covers data representations and processing techniques essential for effective machine learning algorithms.

Machine Learning Fundamentals

Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.

Introduction to Data Stream Processing: Concepts and Applications

Covers the principles of data stream processing and its applications in real-time data analysis.

Dimensionality Reduction: PCA and LDA

Covers dimensionality reduction techniques like PCA and LDA, clustering methods, density estimation, and data representation.

Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Efficient Machine Learning via Data Summarization

Explores efficient machine learning through data summarization, covering challenges, methods, and impactful applications in various domains.

Generalization Error

Explores generalization error in machine learning, focusing on data distribution and hypothesis impact.

Machine Learning: Supervised and Unsupervised Learning Techniques

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Gaussian Naive Bayes & K-NN

Covers Gaussian Naive Bayes, K-nearest neighbors, and hyperparameter tuning in machine learning.

Feature Engineering: Polynomial Regression

Covers fitting linear regression on features of the original predictors for flexible feature representation.

Big Data Ecosystems: Technologies and Challenges

Covers the fundamentals of big data ecosystems, focusing on technologies, challenges, and practical exercises with Hadoop's HDFS.

General Introduction to Data Science

Offers a comprehensive introduction to Data Science, covering Python, Numpy, Pandas, Matplotlib, and Scikit-learn, with a focus on practical exercises and collaborative work.

Page 2 of 2