Applied Machine Learning in Python
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Applied Machine Learning in Python at Coursera Overview
Duration | 34 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Applied Machine Learning in Python at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 3 of 5 in the Applied Data Science with Python Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level
- Approx. 34 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English, Spanish
Applied Machine Learning in Python at Coursera Course details
- 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.
Applied Machine Learning in Python at Coursera Curriculum
Module 1: Fundamentals of Machine Learning - Intro to SciKit Learn
Introduction
Key Concepts in Machine Learning
Python Tools for Machine Learning
An Example Machine Learning Problem
Examining the Data
K-Nearest Neighbors Classification
Course Syllabus
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Notice for Auditing Learners: Assignment Submission
Zachary Lipton: The Foundations of Algorithmic Bias (optional)
Module 1 Quiz
Module 2: Supervised Machine Learning - Part 1
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
A Few Useful Things to Know about Machine Learning
Ed Yong: Genetic Test for Autism Refuted (optional)
Module 2 Quiz
Module 3: Evaluation
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
Practical Guide to Controlled Experiments on the Web (optional)
Module 3 Quiz
Module 4: Supervised Machine Learning - Part 2
Naive Bayes Classifiers
Random Forests
Gradient Boosted Decision Trees
Neural Networks
Deep Learning (Optional)
Data Leakage
Introduction
Dimensionality Reduction and Manifold Learning
Clustering
Conclusion
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)
How to Use t-SNE Effectively
How Machines Make Sense of Big Data: an Introduction to Clustering Algorithms
Post-course Survey
Keep Learning with Michigan Online
Module 4 Quiz
Applied Machine Learning in Python at Coursera Admission Process
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