Applied Data Science: Machine Learning

Description de la formation

Objectif

The Applied Data Science: Machine Learning program will give the students hands-on experience in one of the hottest areas of data science. They will learn tools for predictive modeling and analytics, harnessing the power of neural networks and deep learning techniques across a variety of types of data sets.

Each of the four courses in this program will let them demonstrate their newly-acquired skills through a course project. ECTS credits will be awarded to learners who successfully complete all four courses and course projects as well as a final capstone project.

Contenu

Introduction to Data Analysis with Python

• Getting Started
• Exploring our First Data Set
• The Jupyter Notebook
• A First Look at NumPy
• A First Look at Pandas
• The Basics of Data Visualization
• Probability for Data Science
• Linear Algebra for Data science
• Course Project

Applied Data Analysis

• Getting the Data
• Cleaning the Data
• Manipulating the Data
• Working with Textual Data
• Working with Time-series Data
• Databases in Python
• Statistical Data Analysis
• Course Project

Applied Machine Learning 1

• Introduction to Machine Learning
• Fitting a first model
• Cost functions and outliers
• Linear regressions
• Gradient descent
• Feature engineering
• Regularization
• Course Project

Applied Machine Learning 2

• K-nearest neighbors
• Bias-variance trade-off
• Logistic regressions
• Decision trees and SVMs
• Clustering and dimensionality reduction
• Introduction to deep learning
• Convolutional neural networks
• Chatbots by Swisscom
• Course Project

Capstone Project

 

15 ECTS credits

Lien sur le plan de la formation

• exts.epfl.ch > Admission