Course Summary
This course teaches you how to use Python for machine learning, data analysis, and data visualization. You will learn how to build and evaluate predictive models, as well as how to handle large datasets.Key Learning Points
- Learn Python and machine learning concepts simultaneously
- Understand how to preprocess and visualize data
- Build and evaluate predictive models
- Learn how to handle large datasets
Job Positions & Salaries of people who have taken this course might have
- Data Analyst
- USA: $62,453
- India: ₹4,50,000
- Spain: €29,000
- Machine Learning Engineer
- USA: $112,000
- India: ₹12,00,000
- Spain: €34,000
- Data Scientist
- USA: $121,000
- India: ₹10,00,000
- Spain: €35,000
Related Topics for further study
- Python programming
- Machine learning algorithms
- Data preprocessing
- Data visualization
- Predictive modeling
Learning Outcomes
- Develop a strong foundation in Python programming
- Understand the basics of machine learning algorithms
- Learn how to preprocess, visualize, and model data
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of programming concepts
- Familiarity with statistics and linear algebra
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Applied Data Science with Python
- Introduction to Machine Learning
Related Education Paths
Notable People in This Field
- Andrew Ng
- Kaggle
Related Books
Description
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.
Knowledge
- Describe how machine learning is different than descriptive statistics
- Create and evaluate data clusters
- Explain different approaches for creating predictive models
- Build features that meet analysis needs
Outline
- 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
- Help us learn more about you!
- 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
Summary of User Reviews
Python Machine Learning course on Coursera received positive reviews from students. The course covers Python programming for Machine Learning and provides hands-on experience. The course is highly recommended by students for its practical approach.Key Aspect Users Liked About This Course
The hands-on experience provided by the course is highly appreciated by many students.Pros from User Reviews
- The course covers Python programming for Machine Learning in a practical manner.
- The course provides ample opportunities for students to apply what they learn.
- The course is well-structured and easy to follow.
- The course is taught by knowledgeable instructors who are experts in the field.
- The course provides a solid foundation for further study in Machine Learning.
Cons from User Reviews
- The course may be too basic for those with prior experience in Machine Learning.
- Some students found the pace of the course to be too slow.
- The course may not provide enough depth for advanced Machine Learning topics.
- Some students found the course materials to be outdated.
- Some students found the course to be expensive compared to similar offerings.
Kevyn Collins-Thompson
4.6 Raiting
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