About this course: This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.

Who is this class for: This course is part of “Applied Data Science with Python“ and is intended for learners who have basic python or programming background, and want to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data. Only minimal statistics background is expected, and the first course contains a refresh of these basic concepts. There are no geographic restrictions. Learners with a formal training in Computer Science but without formal training in data science will still find the skills they acquire in these courses valuable in their studies and careers.

Created by: University of Michigan

Taught by: Kevyn Collins-Thompson, Associate Professor
School of Information

Basic Info	Course 3 of 5 in the Applied Data Science with Python Specialization
Level	Intermediate
Language	English
How To Pass	Pass all graded assignments to complete the course.
User Ratings	4.6 stars Average User Rating 4.6See what learners said

Syllabus

WEEK 1

Module 1: Fundamentals of Machine Learning - Intro to SciKit Learn

This module introduces basic machine learning concepts, tasks, and workflow using an example classification problem based on the K-nearest neighbors method, and implemented using the scikit-learn library.

6 videos, 3 readings

Reading: Course Syllabus
Video: Introduction
Reading: Help us learn more about you!
Video: Key Concepts in Machine Learning
Video: Python Tools for Machine Learning
Notebook: Module 1 Notebook
Video: An Example Machine Learning Problem
Video: Examining the Data
Video: K-Nearest Neighbors Classification
Reading: Zachary Lipton: The Foundations of Algorithmic Bias (optional)
Notebook: Assignment 1

Graded: Module 1 Quiz

Graded: Assignment 1 Submission

WEEK 2

Module 2: Supervised Machine Learning - Part 1

This module delves into a wider variety of supervised learning methods for both classification and regression, learning about the connection between model complexity and generalization performance, the importance of proper feature scaling, and how to control model complexity by applying techniques like regularization to avoid overfitting. In addition to k-nearest neighbors, this week covers linear regression (least-squares, ridge, lasso, and polynomial regression), logistic regression, support vector machines, the use of cross-validation for model evaluation, and decision trees.

12 videos, 2 readings

Notebook: Module 2 Notebook
Video: Introduction to Supervised Machine Learning
Video: Overfitting and Underfitting
Video: Supervised Learning: Datasets
Video: K-Nearest Neighbors: Classification and Regression
Video: Linear Regression: Least-Squares
Video: Linear Regression: Ridge, Lasso, and Polynomial Regression
Video: Logistic Regression
Video: Linear Classifiers: Support Vector Machines
Video: Multi-Class Classification
Video: Kernelized Support Vector Machines
Video: Cross-Validation
Video: Decision Trees
Reading: A Few Useful Things to Know about Machine Learning
Reading: Ed Yong: Genetic Test for Autism Refuted (optional)
Notebook: Classifier Visualization Playspace
Notebook: Assignment 2

Graded: Module 2 Quiz

Graded: Assignment 2 Submission

WEEK 3

Module 3: Evaluation

This module covers evaluation and model selection methods that you can use to help understand and optimize the performance of your machine learning models.

7 videos, 1 reading

Notebook: Module 3 Notebook
Video: Model Evaluation & Selection
Video: Confusion Matrices & Basic Evaluation Metrics
Video: Classifier Decision Functions
Video: Precision-recall and ROC curves
Video: Multi-Class Evaluation
Video: Regression Evaluation
Reading: Practical Guide to Controlled Experiments on the Web (optional)
Video: Model Selection: Optimizing Classifiers for Different Evaluation Metrics
Notebook: Assignment 3

Graded: Module 3 Quiz

Graded: Assignment 3 Submission

WEEK 4

Module 4: Supervised Machine Learning - Part 2

This module covers more advanced supervised learning methods that include ensembles of trees (random forests, gradient boosted trees), and neural networks (with an optional summary on deep learning). You will also learn about the critical problem of data leakage in machine learning and how to detect and avoid it.

10 videos, 11 readings

Notebook: Module 4 Notebook
Video: Naive Bayes Classifiers
Video: Random Forests
Video: Gradient Boosted Decision Trees
Video: Neural Networks
Reading: Neural Networks Made Easy (optional)
Reading: Play with Neural Networks: TensorFlow Playground (optional)
Video: Deep Learning (Optional)
Reading: Deep Learning in a Nutshell: Core Concepts (optional)
Reading: Assisting Pathologists in Detecting Cancer with Deep Learning (optional)
Video: Data Leakage
Reading: The Treachery of Leakage (optional)
Reading: Leakage in Data Mining: Formulation, Detection, and Avoidance (optional)
Reading: Data Leakage Example: The ICML 2013 Whale Challenge (optional)
Reading: Rules of Machine Learning: Best Practices for ML Engineering (optional)
Notebook: Assignment 4
Notebook: Unsupervised Learning Notebook
Video: Introduction
Video: Dimensionality Reduction and Manifold Learning
Video: Clustering
Reading: How to Use t-SNE Effectively
Reading: How Machines Make Sense of Big Data: an Introduction to Clustering Algorithms
Video: Conclusion
Reading: Post-course Survey

Graded: Module 4 Quiz

Graded: Assignment 4 Submission

FAQs

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University of Michigan

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Ratings and Reviews

Rated 4.6 out of 5 of 536 ratings

Nice course. Multiple choice quizzes could have been worded a bit better.

Very good course, for people who want to apply Machine Learning without worrying too much about the theoretical aspects of it.

the content of videos , quiz and exercise all work extremely well together towards the stated goal of the course i.e. to give the learner a good over view of how to apply ML theories into action

very good

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Applied Machine Learning in Python

Applied Machine Learning in Python

Applied Machine Learning in Python