Lectures related to Scikit-learn

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.

Data Science Essentials: Python, Numpy, Pandas, and Scikit-learn

Covers the essentials of Data Science using Python, Numpy, Pandas, and Scikit-learn, including DNA sequence analysis and classification.

Unsupervised Behavior Clustering

Explores unsupervised behavior clustering and dimensionality reduction techniques, covering algorithms like K-Means, DBSCAN, and Gaussian Mixture Model.

Data Science Visualization with Pandas

Covers data manipulation and exploration using Python with a focus on visualization techniques.

Logistic Regression: Fundamentals and Applications

Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.

Principal Component Analysis: Eigenfaces

Covers the application of Principal Component Analysis in facial recognition using a famous faces dataset.

Unsupervised Machine Learning: Clustering Basics

Introduces unsupervised machine learning clustering techniques like K-means, Gaussian Mixture Models, and DBSCAN, explaining their algorithms and applications.

Classification pipeline: building and evaluating

Explains building and evaluating a classification pipeline using tweet data sets.

Decision Trees: Classification

Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.

PCA and BBP Phase Transition

Covers PCA application and BBP phase transition in a card game dataset.

Clustering Methods

Covers K-means, hierarchical, and DBSCAN clustering methods with practical examples.

Soft K-means Clustering & DBSCAN

Covers Soft K-means Clustering and DBSCAN principles, algorithms, and comparison.

Regression: High Dimensions

Explores linear regression in high dimensions and practical house price prediction from a dataset.

Machine Learning: Unsupervised Learning and Clustering Techniques

Covers unsupervised learning and clustering methods in machine learning.

Clustering: Understanding Intra-Class Similarity

Explores clustering concepts, emphasizing intra-class similarity and cost-effective methods.

Introduction to Machine Learning: Basics and Examples

Introduces the basics of machine learning, emphasizing the use of Piazza for class-related communications and practical exercises in Python.

Advanced Spark Optimizations and Partitioning

Delves into advanced Spark optimizations, partitioning, data skew, persistency, MLlib, and best practices.

Introduction to Data Science

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Clustering Methods: K-means and DBSCAN

Explores K-means and DBSCAN clustering methods, discussing properties, drawbacks, initialization, and optimal cluster selection.

Decision Trees and Boosting

Introduces decision trees as a method for machine learning and explains boosting techniques for combining predictors.