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.
Buy Now AED 274.99 + VAT
Monthly Subscription Starting at AED 99 + VAT
- Level Professional
- المدة 31 ساعات hours
- الطبع بواسطة University of Michigan
-
Offered by
عن
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.الوحدات
Module 1: Fundamentals of Machine Learning - Intro to SciKit Learn
-
7
Videos
-
4
Readings
-
1
Lab
1
Labs
- Module 1 Notebook
7
Videos
- Introduction
- What's New?
- Key Concepts in Machine Learning
- Python Tools for Machine Learning
- An Example Machine Learning Problem
- Examining the Data
- K-Nearest Neighbors Classification
4
Readings
- Syllabus
- Help us learn more about you!
- Notice for Auditing Learners: Assignment Submission
- Zachary Lipton: The Foundations of Algorithmic Bias (optional)
Week 1 - Assignments
-
1
Programming
-
1
Assignment
- Assignment 1
1
Assignment
- Module 1 Quiz
Module 2: Supervised Machine Learning
-
13
Videos
-
2
Readings
-
2
Lab
2
Labs
- Module 2 Notebook
- Classifier Visualization Playspace
13
Videos
- Introduction to Supervised Machine Learning
- Overfitting and Underfitting
- Supervised Learning: Datasets
- K-Nearest Neighbors: Classification and Regression
- Linear Regression: Least-Squares
- Linear Regression: Ridge, Lasso, and Polynomial Regression
- Logistic Regression
- Linear Classifiers: Support Vector Machines
- Multi-Class Classification
- Kernelized Support Vector Machines
- Cross-Validation
- Decision Trees
- One-Hot Encoding (Optional)
2
Readings
- A Few Useful Things to Know about Machine Learning
- Ed Yong: Genetic Test for Autism Refuted (optional)
Week 2 - Assignments
-
1
Programming
-
1
Assignment
- Assignment 2
1
Assignment
- Module 2 Quiz
Module 3: Evaluation
-
8
Videos
-
1
Readings
-
1
Lab
1
Labs
- Module 3 Notebook
8
Videos
- Model Evaluation & Selection
- Confusion Matrices & Basic Evaluation Metrics
- Classifier Decision Functions
- Precision-Recall and ROC Curves
- Multi-Class Evaluation
- Regression Evaluation
- Model Selection: Optimizing Classifiers for Different Evaluation Metrics
- Model Calibration (Optional)
1
Readings
- Practical Guide to Controlled Experiments on the Web (optional)
Week 3 - Assignments
-
1
Readings
-
1
Programming
-
1
Assignment
- Assignment 3
1
Assignment
- Module 3 Quiz
1
Readings
- Note on Assignment 3
Module 4: Supervised Machine Learning - Part 2
-
6
Videos
-
8
Readings
-
1
Lab
1
Labs
- Module 4 Notebook
6
Videos
- Naive Bayes Classifiers
- Random Forests
- Gradient Boosted Decision Trees
- Neural Networks
- Deep Learning (Optional)
- Data Leakage
8
Readings
- Neural Networks Made Easy (optional)
- Play with Neural Networks: TensorFlow Playground (optional)
- Deep Learning in a Nutshell: Core Concepts (optional)
- Assisting Pathologists in Detecting Cancer with Deep Learning (optional)
- The Treachery of Leakage (optional)
- Leakage in Data Mining: Formulation, Detection, and Avoidance (optional)
- Data Leakage Example: The ICML 2013 Whale Challenge (optional)
- Rules of Machine Learning: Best Practices for ML Engineering (optional)
Week 4 - Assignments
-
1
Programming
-
1
Assignment
- Assignment 4
1
Assignment
- Module 4 Quiz
Optional: Unsupervised Machine Learning
-
3
Videos
-
2
Readings
-
1
Lab
1
Labs
- Unsupervised Learning Notebook
3
Videos
- Introduction
- Dimensionality Reduction and Manifold Learning
- Clustering
2
Readings
- How to Use t-SNE Effectively
- How Machines Make Sense of Big Data: an Introduction to Clustering Algorithms
Conclusion
-
1
Videos
-
3
Readings
1
Videos
- Conclusion
3
Readings
- Post-course Survey
- Keep Learning with Michigan Online
- Admissions Team alert about fee waiver
Auto Summary
"Applied Machine Learning in Python" is a professional course offered by Coursera, focusing on practical techniques and methods in machine learning rather than statistical theory. Ideal for learners with a background in Python, it covers clustering, predictive modeling, feature engineering, and advanced techniques like building ensembles. The course spans 1860 minutes and is available through Starter, Professional, and Paid subscriptions. Perfect for individuals aiming to enhance their data science and AI skills.
Kevyn Collins-Thompson