Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Course Overview
Applied Machine Learning in Python an intermediate-level course administered by the University of Michigan. The learners will be exposed to applied machine learning in python. Applied Machine Learning in Python Certification Syllabus, developed by Kevyn Collins-Thompson, the Associate Professor at the School of Information, will walk the students through many aspects of applied machine learning, especially the techniques and methods.
Provided by Coursera, the Applied Machine Learning in Python Certification Course helps the students to have a deep knowledge of building ensembles, practical limitations of predictive models, supervised and unsupervised techniques, and the like. Applied Machine Learning in Python Certification by Coursera is the third course in Applied Data Science with Python Specialization.
The Highlights
- Provided by Coursera
- Approximately 31 hours of programme
- Offered by the University of Michigan
- Flexible Deadlines
- Self-Paced Learning Option
- Intermediate Level Course
- Shareable Certificate
- Financial Aid Available
- 100% Online Course
Programme Offerings
- English Videos
- practice quizzes
- Graded Assignments with peer feedback
- graded Quizzes with feedback
- Graded Programming Assignments
- Course Videos & Readings
- EMI payment options
- 14 day refund period.
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | Coursera |
The Applied Machine Learning in Python Certification Fee is tabulated below based on the number of months the students want to stay on the programme:
Description | Total Fee in INR |
Course Fee, 1 month | Rs. 4,117 |
Course Fee, 3 months | Rs. 8,234 |
Course Fee, 6 months | Rs. 12,352 |
Eligibility Criteria
Certification Qualifying Details
The Applied Machine Learning in Python Certification will be provided to the learners who have duly finished all the aspects of the programme including the course materials, readings, videos, quizzes, and assignments.
What you will learn
By the end of Applied Machine Learning in Python Training, the learners learn the following concepts:
- Python Programming
- Machine Learning (ML) Algorithms
- Machine Learning
- Scikit-Learn
- Creation and evaluation of data clusters
- Predictive models creation
Who it is for
Applied Machine Learning in Python Classes is a better option for the professionals including
- ML Engineer
- Python Programmer
- Programmer
Admission Details
Step 1- At first, the students will have to register and sign up on https://www.coursera.org/ to get access to the programmes offered by Coursera.
Step 2 - After activating the Coursera account, the candidate can sign in.
Step 3 - Then, the candidate can search the ‘University of Michigan’ in the search column and then, the courses offered by University of Michigan will appear on the screen.
Step 4 - Then, find the course ‘Applied Machine Learning in Python’ in the list and click on it.
Step 5- Then, the page of the course will appear on the screen and then, click on the option ‘enroll’. The students can enroll in the programme either by free of cost or paying the fee prescribed by Coursera.
The Syllabus
Videos
- Introduction
- Key Concepts in Machine Learning
- Python Tools for Machine Learning
- An Example Machine Learning Problem
- Examining the Data
- K-Nearest Neighbors Classification
Readings
- Course Syllabus
- Help us learn more about you!
- Notice for Auditing Learners: Assignment Submission
- Zachary Lipton: The Foundations of Algorithmic Bias (optional)
- Syllabus
Quiz
- Module 1 Quiz
Programming Assignment
- Assignment 1
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
Readings
- A Few Useful Things to Know about Machine Learning
- Ed Yong: Genetic Test for Autism Refuted (optional)
Quiz
- Module 2 Quiz
Programming Assignment
- Assignment 2
Ungraded lab
- Module 2 Notebook
- Classifier Visualization Playspace
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)
Reading
- Practical Guide to Controlled Experiments on the Web (optional)
- Note on Assignment 3
Quiz
- Module 3 Quiz
Programming Assignment
- Assignment 3
Ungraded lab
- Module 3 Notebook
Videos
- Naive Bayes Classifiers
- Random Forests
- Gradient Boosted Decision Trees
- Neural Networks
- Deep Learning (Optional)
- Data Leakage
- Introduction
- Dimensionality Reduction and Manifold Learning
- Clustering
- Conclusion
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)
- 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
Quiz
- Module 4 Quiz
Programming Assignment
- Assignment 4
Ungraded lab
- Module 4 Notebook
- Unsupervised Learning Notebook
Ungraded lab
- Module 1 Notebook
Instructors
UM–Ann Arbor Frequently Asked Questions (FAQ's)
The University of Michigan is offering the course.
The course is instructed by Kevyn Collins-Thompson who is the Associate Professor at the School of Information.
The subtitles are available in the languages of Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English and Spanish.
Yes, the programme is offered in 100% mode and the students can attend the programme from anywhere.
No, the job assistance is not available after the course.