Об этом курсе: 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.
Для кого этот курс: 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.
Преподаватели: Kevyn Collins-Thompson, Associate Professor
School of Information
| Основные сведения | Курс 3 из 5 — Applied Data Science with Python Specialization |
| Уровень | Учащийся среднего уровня |
| Язык | English, Субтитры: Korean |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
- Reading: Course Syllabus
- Reading: Help us learn more about you!
- Видео: Key Concepts in Machine Learning
- Видео: Python Tools for Machine Learning
- Reading: Notice for Auditing Learners: Assignment Submission
- Notebook: Module 1 Notebook
- Видео: An Example Machine Learning Problem
- Видео: Examining the Data
- Видео: K-Nearest Neighbors Classification
- Reading: Zachary Lipton: The Foundations of Algorithmic Bias (optional)
- Notebook: Assignment 1
- Notebook: Module 2 Notebook
- Видео: Overfitting and Underfitting
- Видео: Supervised Learning: Datasets
- Видео: K-Nearest Neighbors: Classification and Regression
- Видео: Linear Regression: Least-Squares
- Видео: Linear Regression: Ridge, Lasso, and Polynomial Regression
- Видео: Logistic Regression
- Видео: Linear Classifiers: Support Vector Machines
- Видео: Multi-Class Classification
- Видео: Kernelized Support Vector Machines
- Видео: Cross-Validation
- Видео: 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
- Видео: Confusion Matrices & Basic Evaluation Metrics
- Видео: Classifier Decision Functions
- Видео: Precision-recall and ROC curves
- Видео: Multi-Class Evaluation
- Видео: Regression Evaluation
- Reading: Practical Guide to Controlled Experiments on the Web (optional)
- Видео: Model Selection: Optimizing Classifiers for Different Evaluation Metrics
- Notebook: Assignment 3
- Notebook: Module 4 Notebook
- Видео: Random Forests
- Видео: Gradient Boosted Decision Trees
- Видео: Neural Networks
- Reading: Neural Networks Made Easy (optional)
- Reading: Play with Neural Networks: TensorFlow Playground (optional)
- Видео: Deep Learning (Optional)
- Reading: Deep Learning in a Nutshell: Core Concepts (optional)
- Reading: Assisting Pathologists in Detecting Cancer with Deep Learning (optional)
- Видео: 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
- Видео: Introduction
- Видео: Dimensionality Reduction and Manifold Learning
- Видео: Clustering
- Reading: How to Use t-SNE Effectively
- Reading: How Machines Make Sense of Big Data: an Introduction to Clustering Algorithms
- Видео: Conclusion
- Reading: Post-course Survey
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Everything builds up very nicely on top of each other. A qualm some might have is that part of the assessments might be very simple. However, this is an applied course and the course material stays true to what it promises.
It gives a great overview of different machine learning methods. I found useful information that can be missing in other ML courses. Great course!
Quiz material were great!
MR
Covers most of the basic supervised Machine learning Algorithms in SciKit-Learn from application POV.