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.
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 |
- Reading: Course 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
- 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
- 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
- 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
- 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
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| Purchase Course | Audit | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Available | Not available |
| Receive a final grade | Available | Not available |
| Earn a shareable Course Certificate | Available | Not available |
After this course you will be able to do your own analysis using machine learning which is really great.
Provide a quick and good overview of important, popular machine learning topics and their practical use with Python scikit-learn module. The material covers the important parameters to keep a watch on for performance and highlights the usual pitfalls and missteps. Very practical learning, makes one comfortable using ML tools and quickly apply for real problems like in the last assignment.
This course was challenging and extremely interesting. The long and detailed lectures and excellent lecture notes covered the material very thoroughly for an online course.
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Excellent course. A practical application of the concepts in Python/sklean.