Spark Data Frames: Overview and Performance Analysis

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Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.

Introduction to Applied Data Analysis

Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.

Introduction to Machine Learning: Course Overview and Basics

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

Big Data Challenges: Scaling to Massive Data

Explores challenges of handling massive data in the era of big data, discussing solutions like MapReduce and Spark.

Regression: High Dimensions

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

Deep Learning: Principles and Applications

Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.

PyTorch and Convolutional Networks

Covers PyTorch tensor data structure and training a CNN to classify images.

Neural Networks: Training and Activation

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Data Representations and Processing in Machine Learning

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

Principal Component Analysis: Eigenfaces

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