Duration
28 hours (usually 4 days including breaks)
Requirements
Knowledge of Python programming language. Basic familiarity with statistics and linear algebra is recommended.
Overview
The aim of this course is to provide general proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.
Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
Course Outline
Introduction to Applied Machine Learning
- Statistical learning vs. Machine learning
- Iteration and evaluation
- Bias-Variance trade-off
Supervised Learning and Unsupervised Learning
- Machine Learning Languages, Types, and Examples
- Supervised vs Unsupervised Learning
Supervised Learning
- Decision Trees
- Random Forests
- Model Evaluation
Machine Learning with Python
- Choice of libraries
- Add-on tools
Regression
- Linear regression
- Generalizations and Nonlinearity
- Exercises
Classification
- Bayesian refresher
- Naive Bayes
- Logistic regression
- K-Nearest neighbors
- Exercises
Cross-validation and Resampling
- Cross-validation approaches
- Bootstrap
- Exercises
Unsupervised Learning
- K-means clustering
- Examples
- Challenges of unsupervised learning and beyond K-means
Neural networks
- Layers and nodes
- Python neural network libraries
- Working with scikit-learn
- Working with PyBrain
- Deep Learning