Applied Machine Learning in Python

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Opleiderscore: starstarstarstar_borderstar_border 6,3 Coursera (CC) heeft een gemiddelde beoordeling van 6,3 (uit 4 ervaringen)

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Beschrijving

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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. cro…

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When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

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 Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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University of Michigan The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.

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 expand


  1. Reading: Course Syllabus
  2. Video: Introduction
  3. Reading: Help us learn more about you!
  4. Video: Key Concepts in Machine Learning
  5. Video: Python Tools for Machine Learning
  6. Notebook: Module 1 Notebook
  7. Video: An Example Machine Learning Problem
  8. Video: Examining the Data
  9. Video: K-Nearest Neighbors Classification
  10. Reading: Zachary Lipton: The Foundations of Algorithmic Bias (optional)
  11. 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 expand


  1. Notebook: Module 2 Notebook
  2. Video: Introduction to Supervised Machine Learning
  3. Video: Overfitting and Underfitting
  4. Video: Supervised Learning: Datasets
  5. Video: K-Nearest Neighbors: Classification and Regression
  6. Video: Linear Regression: Least-Squares
  7. Video: Linear Regression: Ridge, Lasso, and Polynomial Regression
  8. Video: Logistic Regression
  9. Video: Linear Classifiers: Support Vector Machines
  10. Video: Multi-Class Classification
  11. Video: Kernelized Support Vector Machines
  12. Video: Cross-Validation
  13. Video: Decision Trees
  14. Reading: A Few Useful Things to Know about Machine Learning
  15. Reading: Ed Yong: Genetic Test for Autism Refuted (optional)
  16. Notebook: Classifier Visualization Playspace
  17. 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 expand


  1. Notebook: Module 3 Notebook
  2. Video: Model Evaluation & Selection
  3. Video: Confusion Matrices & Basic Evaluation Metrics
  4. Video: Classifier Decision Functions
  5. Video: Precision-recall and ROC curves
  6. Video: Multi-Class Evaluation
  7. Video: Regression Evaluation
  8. Reading: Practical Guide to Controlled Experiments on the Web (optional)
  9. Video: Model Selection: Optimizing Classifiers for Different Evaluation Metrics
  10. 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 expand


  1. Notebook: Module 4 Notebook
  2. Video: Naive Bayes Classifiers
  3. Video: Random Forests
  4. Video: Gradient Boosted Decision Trees
  5. Video: Neural Networks
  6. Reading: Neural Networks Made Easy (optional)
  7. Reading: Play with Neural Networks: TensorFlow Playground (optional)
  8. Video: Deep Learning (Optional)
  9. Reading: Deep Learning in a Nutshell: Core Concepts (optional)
  10. Reading: Assisting Pathologists in Detecting Cancer with Deep Learning (optional)
  11. Video: Data Leakage
  12. Reading: The Treachery of Leakage (optional)
  13. Reading: Leakage in Data Mining: Formulation, Detection, and Avoidance (optional)
  14. Reading: Data Leakage Example: The ICML 2013 Whale Challenge (optional)
  15. Reading: Rules of Machine Learning: Best Practices for ML Engineering (optional)
  16. Notebook: Assignment 4
  17. Notebook: Unsupervised Learning Notebook
  18. Video: Introduction
  19. Video: Dimensionality Reduction and Manifold Learning
  20. Video: Clustering
  21. Reading: How to Use t-SNE Effectively
  22. Reading: How Machines Make Sense of Big Data: an Introduction to Clustering Algorithms
  23. Video: Conclusion
  24. Reading: Post-course Survey

Graded: Module 4 Quiz
Graded: Assignment 4 Submission

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