Associate Professor
Applied Machine Learning in Python
What You'll Learn
- Describe how machine learning is different than descriptive statistics
- Create and evaluate data clusters
- Explain different approaches for creating predictive models
- Build features that meet analysis needs
4 Modules
32 Hours
8 hrs per module (approx.)
Rating
About Applied Machine Learning in Python
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 through a tutorial. 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.
Skills You'll Gain
- Machine Learning
- Python For Data Analysis
- Python (Programming Language)
What You'll Earn
- Certificate of Completion:
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Welcome Message
Welcome to Applied Machine Learning in Python, a course focused on practical machine learning techniques rather than theoretical statistics. You will explore supervised and unsupervised learning, feature engineering, model evaluation, and ensemble methods using Python and scikit-learn. The course emphasizes real-world application and reproducible analysis workflows.
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.
Course Schedule
Modules 1: Fundamentals of Machine Learning - Intro to SciKit Learn
- Reading: Syllabus
- Video: Introduction
- Reading: Help us learn more about you!
- Video: Key Concepts in Machine Learning
- Video: Python Tools for Machine Learning
- Reading: Notice for Auditing Learners: Assignment Submission
- Ungraded Lab: 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)
- Ungraded Lab: Assignment 1
- Graded: Module 1 Quiz
- Graded: Assignment 1 Submission
Module 2: Supervised Machine Learning - Part 1
- Ungraded Lab: 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)
- Ungraded Lab: Classifier Visualization Playspace
- Ungraded Lab: Assignment 2
- Graded: Module 2 Quiz
- Graded: Assignment 2 Submission
Module 3: Evaluation
- Ungraded Lab: 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
- Ungraded Lab: Assignment 3
- Graded: Module 3 Quiz
- Graded: Assignment 3 Submission
Module 4: Supervised Machine Learning - Part 2
- Ungraded Lab: 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)
- Ungraded Lab: Assignment 4
- Ungraded Lab: 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
- Graded: Module 4 Quiz
- Graded: Assignment 4 Submission
Grading Policy
Learners must complete quizzes and programming assignments. Quizzes are worth 20% of your total grade and programming assignments are worth 80% of your total grade.
Intermediate Level
Some related experience required
